生物信息挖掘新基因预测平台的建立与TSEG-3的克隆和功能初步研究
|
摘要
第一部分：生物信息数据挖掘新基因预测平台的建立摘要
目的：依据最新生物信息学数据库和数据挖掘技术,充分挖掘现有生物数据库内蕴含信息,建立基于生物信息和数据挖掘技术的新基因克隆和功能预测平台。
方法：首先,通过检索词从dbEST数据库检索,下载研究相关目标序列,将所选序列进行Blast分析,比对出相应的Unigene同源簇,下载上述同源簇或通过数字差异显示工具NCBI_DDD检索组织特异性Unigene同源簇；其次,通过Biolign软件进行EST clusters的拼接、延伸和组装,应用Blast检索程序进行EST clusters拼接的contigs序列基因组相似性分析,校正contigs组装产生的误差；第三,应用Genscan程序分析基因组同源序列的编码区(CDS)；第四,DNAssist预测上述CDS序列的开放阅读框(open reading frame, ORF);最后,Premier 5.0设计针对每个ORF的对应引物。
结果：通过数字差异显示工具分析RIKEN full-length enriched mouse cDNA library和Epididymis cDNA library间的表达序列标签的差异序列,得到一个在RIKEN full-length enriched mouse cDNA library高丰度表达的同源簇Mm.5168,下载其对应的同源簇为UGID:258077;进行后续EST组装、延伸和contigs序列校对,同时预测基因组同源性编码区和开放阅读框,并设计相应的引物。
结论：生物信息数据挖掘新基因克隆和功能预测平台能有效的发现组织特异性Unigene同源簇,为未知新基因的克隆和功能预测提供了很好的方向。
第二部分：小鼠睾丸特异表达基因TSEG-3的克隆、组织定位及表达谱分析
目的：应用生物信息数据挖掘新基因克隆和功能预测平台,预测可能参与睾丸精子发生和男性不育相关睾丸基因,并通过实验验证预测的准确性。
方法：首先,利用数字差异显示工具Digital Differential Display检索与EST片段BY706707.1的同源簇,按前文提出的生物信息数据挖掘新基因克隆和功能预测流程,预测出一个新的编码区序列；其次,通过RT-PCR验证,证实其确实存在于成年小鼠睾丸；第三,通过T／A克隆,进行测序分析其与设计序列的一致性和同源性,验证了新基因预测平台的可靠程度；通过原位杂交分析分析TSEG.3表达的细胞类型,同时以用Northern blotting分析转录本大小和RT-PCR分析TSEG-3多器官和不同发育阶段表达谱序列。
结果：成功从小鼠睾丸组织中扩增出TSEG-3,测序结果与预测结果一致,将基因序列提交Genbank数据库,获得Genbank登录号：EU477370(GI：186892498),命名为睾丸特异表达基因3(TSEG-3)。原位杂交结果显示TSEG-3表达于精原细胞、少量精母细胞和精子细胞等,Northern Blotting印迹证实转录长度为1023bp左右,小鼠TSEG-3多组织表达谱分析显示TSEG-3特异地表达于睾丸组织,在出生后2月龄时达到表达峰值。
结论：TSEG-3是睾丸特异性表达基因,具有发育阶段特异性特征,可能参与小鼠生精过程,深入研究TSEG-3的功能有助于阐明精子发生机制和男性不育机制。
第三部分：小鼠睾丸特异表达基因TSEG-3生物信息学分析及TSEG-3多克隆抗体制备与鉴定
目的：应用生物信息数据挖掘工具,进行TSEG-3蛋白生物信息学分析,为进一步研究TSEG-3蛋白的功能提供必要方向。
方法：应用序列信息挖掘软件,分析TSEG-3蛋白质物理特性、疏水区域和亲水区、跨膜区、蛋白质二级结构、蛋白质三级结构、拓朴树分析、特异性磷酸化位点、抗原位点及肽段、亚细胞定位及TSEG-3蛋白功能注释；设计TSEG-3抗原多肽并合成多肽,制备抗TSEG-3蛋白血清,Western Blotting鉴定TSEG-3蛋白分子量大小。
结果：通过序列分析显示TSEG-3位于第17号染色体17A3.3区,有六个外显子组成。TSEG-3属于DFU634家族基因,在TSEG-3的5’侧翼区域有六个启动子序列；miRNA检索发现16个可能相关miRNA;并在TSEG.3第277AA.285AA预测出一个非稳定区段(disordered regions)序列为：QSLHEALFG；TSEG-3属于非跨膜蛋白,47.8%可能胞质的：21.7%定位于细胞核；理论等电点为7.15.吸光系数为23680；具有10个丝氨酸(Serine)位点,3个酪氨酸(Tyrosine)位点,在第211到221间存在一个PKC位点,第28氨基酸残基处为信号肽区,第65个氨基酸为一个精氨酸／赖氨酸前肽切割位点(Arg(R)／Lys(K))等；TSEG-3蛋白预测理论分子量为38,52976KD和Western Blotting检测结果一致；功能预测提示TSEG-3可能参与生殖细胞的分化和凋亡。
结论：生物信息数据挖掘工具为新基因TSEG-3功能研究提供了丰富的研究内容,极大减少了TSEG-3功能研究的盲目性,加快了基因功能鉴定的速度。
第四部分：过表达TSEG-3基因对小鼠精母细胞系GC-2 spd增殖、凋亡的影响
目的：探讨TSEG-3过表达对小鼠精母细胞系GC-2 spd(ts)的增殖和凋亡的影响。
方法：通过构建pEGFP-TSEG-3载体和体外培养小鼠精母细胞系GC-2 spd(ts),观察融合蛋白EGFP-TSEG-3的亚细胞定位；采用MTT法检测TSEG-3过表达对GC-2spd(ts)细胞增殖的影响；应用流式细胞仪分析TSEG-3过表达对GC-2 spd(ts)细胞周期的作用；AO/EB双重染色法、Hoechst 33258/PI双染、Annexin V/PI双染法和JC-1染色分析TSEG-3过表达对GC-2 spd(ts)细胞凋亡率的影响；Real-Time RT-PCR分析TSEG-3过表达对凋亡相关蛋白的影响。
结果：融合蛋白EGFP-TSEG-3定位于细胞核,MTT法结果提示TSEG-3过表达抑制GC-2 spd(ts)细胞增殖；TSEG-3过表达诱导GC-2 spd(ts)细胞出现G1和G2／M期捕获。TSEG-3过表达诱导GC-2 spd(ts)细胞凋亡,Real-Time RT-PCR显示TSEG-3过表达诱导Fas上调,同时Bcl-2/Bax比率下调,说明Fas-Fas通路和Bcl-2/Bax均参与TSEG-3过表达诱导GC-2 spd(ts)细胞凋亡。
结论：TSEG-3过表达抑制GC-2 spd(ts)细胞增殖,诱导GC-2 spd(ts)细胞凋亡,Fas/Fas通路和Bcl-2/Bax均参与TSEG-3过表达诱导GC-2 spd(ts)细胞凋亡途径。
第五部分：TSEG-3过表达诱导小鼠睾丸精原细胞凋亡及其睾丸疾病模型表达谱分析
目的：整体水平探讨TSEG-3过表达对小鼠睾丸精原细胞的影响,并分析其在睾丸疾病模型表达谱。
方法：制备in vivo-jetPEITM-pEGFP-TSEG-3聚合物：构建TSEG-3过表达模型,HE染色和Tunel分析TSEG-3过表达对小鼠生殖细胞的影响；同时构建手术隐睾模型,分析TSEG-3转录水平与细胞凋亡率的关系,分析温度对TSEG-3转录水平的影响；复制17β雌二醇诱导隐睾模型,检测17β雌二醇诱导隐睾模型中TSEG-3转录水平,明确17β雌二醇对TSEG-3转录的影响。
结果：荧光显微镜观察提示In Vivo JetPEITM是一种高效、可靠安全的DNA转染试剂,能有效的携带外源DNA入生殖细胞。TSEG-3过表达使睾丸组织生精小管官腔内细胞密度减少,生精小管官腔变薄,大量精原细胞及精母细胞缺失,未发现成熟精子。Tunel结果提示TSEG-3过表达可能诱导生精细胞的凋亡。手术隐睾模型结果显示：TSEG-3的转录激活与手术组小鼠睾丸内生精细胞凋亡率增加呈正相关关系,进一步提示TSEG-3可能参与睾丸组织生精过程中细胞凋亡过程。17β-雌二醇诱导隐睾模型原位杂交结果提示17β-雌二醇可能参与抑制不同阶段生殖细胞中TSEG-3的转录。
结论：TSEG-3过表达诱导小鼠睾丸生殖细胞凋亡,温度可能诱导TSEG-3的转录,17β-雌二醇可能参与抑制不同阶段生殖细胞中TSEG-3的转录。
PART I Establishing of novel gene prediction platform based on bioinformatics data mining
Objective:According to the latest development of bioinformatics databases and data mining techniques, to fully exploit the valuable information of the existing biological database and build the new technology platforms of cloning and functional prediction based on bioinformatics and data mining.
Method:Search dbEST database and download target sequence involved in the research. The analysis of the selected sequence will be alignmented using the program of Blast in mouse genomic. Download the clusters of Unigene and the splicing, extension and assembly of EST clusters were carried out using Biolign software. The assembled contigs of EST clusters are analyzed by Blast_N in mouse genomics. Coding region sequences (CDS) of the homologous sequences of the assembled contigs in Genome were predicted via Genscan program. ORF sequences of the CDS were predicted by DNAssist. Each primers for ORF were designed by Primer Premier 5.0.
Results:The cDNA library of Mm.5168 was searched by the digital differential display between RIKEN full-length enriched mouse cDNA library and Epididymis cDNA library. The sequences of Unigene UGID:258077 for Mm.5168 was download. Some new CDS was predicted by Genescan and the open reading frame for each CDS was analyzed by the DNAssist.
Conclusion:The new gene prediction platform based on bioinformatics and data mining is a effective tool in searching the novel unknown genes and predicting the function of novel gene and provides a good direction for study of the novel gene.
PARTⅡThe cloning, cellular location of mouse testis-specific gene 3 and the analysis of expression profiles for TSEG-3
Objective:To predict the novel testis genes involved in spermatogenesis and male infertility using the novel gene prediction platform based on bioinformatics data mining and accuracy of prediction will be verified by RT-PCR.
Method:the homology EST clusters of BY706707.1 was found by using Digital Differential Display (DDD). The CDS for a novel gene was predicted by the novel gene prediction platform based on bioinformatics data mining. We detected the expression of the novel gene by RT-PCR. The cDNA of the novel gene was cloned into pGM-T and The cDNA of the novel gene was sequenced. The cellular location of the novel gene was analyzed by in situ hybridization. The length of transcript for the novel gene TSEG-3 was verified by nothern blotting. The expression profiles of the novel gene was analyzed by RT-PCR.
Results:A novel gene was amplified from adult mouse testis and named TSEG-3. No sequences were consistent with the sequence of TSEG-3 in Genebank database. The sequence of the novel gene TSEG-3 was consistent with the sequence of prediction. Brown Sediment for Hybridization signal of TSEG-3 mRNA was found in spermatogonia, spermatocytes, and a small amount of sperm cells, etc. The length of transcript of TSEG-3 is 1023bp by northern blotting. The transcript of TSEG-3 was detected only in testis tissue and up to peak at postnatal 2 months.
Conclusion:TSEG-3 is a testis specifically expressed gene and specific devolopment stages gene. TSEG-3 may participate in spermategensis and the study of TSEG-3 maybe help to eluciate the molecular mechanism of male infertility.
PART III Bioinformatics analysis of mouse testis-specific gene 3, the production of TSEG-3 polyclonal antibody and identification of TSEG-3 protein.
Objective:To provide the necessary research directions for TSEG-3 and to further study the function of TSEG-3 protein, the bioinformatics analysis of the mouse testis-specific gene 3 was executed via the tool based on bioinformatics & data mining tools.
Method:Physical property, hydrophobic regions, hydrophilic regions, transmembrane region, protein secondary structure, protein structure, phylogenetic tree, specific phosphorylation sites, antigenic sites and the peptides, subcellular localization and TSEG-3 protein functional annotation was predicted using the sequences'data mining software. Results:The theoretical molecular weight of TSEG-3 protein was 38529.76 Dalton, and its theoretical isoelectric point was 7.15. A putative conserved domain DUF634 was detected between 110 and 310 residues of TSEG-3 by National Biological Information Center (NCBI) blast_p program. The phylogenetic tree and homology tree indicated that TSEG-3 had a closest genetic relationship with rat LOC294301, and close genetic relationships with human C6orf81, Canis LOC474886, Bos taurus LOC540812, and Equus caballus LOC100053594. The nucleic acid and amino acid sequences of TSEG-3 were 99% homologous to those of 4930511I11Rik. NetPhos 1.0 Server showed a protein kinase C (PKC) phosphorylation site at the 224th residue of TSEG-3 protein. A propeptide cleavage site was found at the 56th residue of TSEG-3 protein by ProPv.l.Ob program. A signal peptide site was noted at the 28th residue of TSEG-3 protein by SignalP 3.0 Server. The PSORTⅡprogram showed that TSEG-3 protein localized in cytoplasm (47.8% possibility) or nuclei (21.7% possibility). The prediction of the PFP Server indicated that TSEG-3 might participate in cell differentiation and induction of apoptosis.
Conclusion:The function study of TSEG-3 protein was provided in different directions using bioinformatics & data mining tools, greatly reducing the blindness of the TSEG-3 function study, accelerated the pace of the identification of the novel gene function.
PART IV Overexpression of TSEG-3 inhibited mouse spermatogonial cell lines GC-2 spd cell proliferation and induce apoptosis
Objective:To analyze the regulation of proliferation and apoptosis for mouse spermatogonial cell lines GC-2 spd(ts) cell post-overexpression of TSEG-3 in vitro.
Method:Firstly, to watch the subcellular of TSEG-3 protein, the plasmid pEGFP-TSEG-3 was constructed. The proliferation of GC-2 spd(ts) cell was evaluated by MTT post-overexpression of TSEG-3. The cell cycle of GC-2 spd(ts) cell was detected by FCM after overexpression of TSEG-3. Apoptosis ratio of GC-2 spd(ts) cell was analyzed by FCM via AO/EB double staining, Hoechst 33258/PI double staining, Annexin V/PI double staining and the change of Mitochondrial membrane potential was evaluated by FCM via JC-1 staining. The transcript of apoptosis pathway related genes was detected by real-time quantitative RT-PCR.
Results:EGFP-TSEG-3 fusion protein is located in the nuclear of cos-7,239 cell and GC-2 spd(ts) cell. The proliferation of GC-2 spd(ts) cell was inhibited post overexpression of TSEG-3 via MTT. G2 and G2/M of GC-2 spd(ts) cell was arrested by FCM via PI staining after overexpression of TSEG-3. Overexpression of TSEG-3 induced the apoptosis of GC-2 spd(ts) cell. The transcript of Fas was up-regulated and the ratio of Bcl-2/Bax was down-regulted post overexpression of TSEG-3.
Conclusion:Overexpression of TSEG-3 inhibits the proliferation of GC-2 spd (ts) cell, and induces the apoptosis of GC-2 spd (ts) cells. Fas/Fas pathway and Bcl-2/Bax were involved in the process that overexpression of TSEG-3 induced the apoptosis of GC-2 spd (ts).
PART V TSEG-3 overexpression induced mouse testis germ cell apoptosis in vivo and expression pattern analysis of TSEG-3 in different cryptorchidism models
Objective:To explore the changes of mouse testis germ cell via the overexpression of TSEG-3, analyze the relationship between TSEG-3 and apoptosis of mouse testis germ cell in vivo and detected the expression profiles in different cryptorchidism models.
Method:Firstly, the polymer of in vivo-jetPEI TM-pEGFP-TSEG-3 was prepared according to standard protocol. The histological changes and apoptosis cells was evaluated by HE staining and Tunel after 72 hours post TSEG-3 overexpression in mouse testis. Secondly, Surgical cryptorchidism model was constructed according to the previous procedure. The TSEG-3 transcription level in testis of surgical cryptorchidism model was detected by real-time RT-PCR. The relationship between germ cell apoptosis and testis of surgical cryptorchidism was analyzed via Tunel staining. Thirdly, the 17β-estradiol induced cryptorchidism model was build using previous method. The transcript of TSEG-3 in testis tissues was analyzed by in situ hybridzation and real-time RT-PCR.
Results:In Vivo JetPEITM is an efficient, reliable and safe transfection reagent for plasmid DNA via fluorescence microscope and can effectively carry exogenous DNA into the germ cells. The number and cell density of seminiferous tubules reduced in testis of TSEG-3 overexpression model. Seminiferous tubule in testis of TSEG-3 overexpression model is thinner than control group. There were the loss of spermatogonia and spermatocyte, and not any mature sperm. Tunel results suggest TSEG-3 overexpression may induce germ cell apoptosis. In surgical cryptorchidism model, the transcript of TSEG-3 was up-regulated and positive correlation with testicular germ cell apoptosis. These resluts showed TSEG-3 may be involved in the process of testicular germ cell apoptosis. But, results of in situ hybridization and real-time RT-PCR suggest 17β-estradiol inhibited the TSEG-3 transcription in different stages of germ cells.
Conclusion:TSEG-3 overexpression induced germ cells apoptosis in vivo. Temperature may induce TSEG-3 transcription and 17β-estradiol maybe inhibit the TSEG-3 transcription in different stages of germ cells.
目的：依据最新生物信息学数据库和数据挖掘技术,充分挖掘现有生物数据库内蕴含信息,建立基于生物信息和数据挖掘技术的新基因克隆和功能预测平台。
方法：首先,通过检索词从dbEST数据库检索,下载研究相关目标序列,将所选序列进行Blast分析,比对出相应的Unigene同源簇,下载上述同源簇或通过数字差异显示工具NCBI_DDD检索组织特异性Unigene同源簇；其次,通过Biolign软件进行EST clusters的拼接、延伸和组装,应用Blast检索程序进行EST clusters拼接的contigs序列基因组相似性分析,校正contigs组装产生的误差；第三,应用Genscan程序分析基因组同源序列的编码区(CDS)；第四,DNAssist预测上述CDS序列的开放阅读框(open reading frame, ORF);最后,Premier 5.0设计针对每个ORF的对应引物。
结果：通过数字差异显示工具分析RIKEN full-length enriched mouse cDNA library和Epididymis cDNA library间的表达序列标签的差异序列,得到一个在RIKEN full-length enriched mouse cDNA library高丰度表达的同源簇Mm.5168,下载其对应的同源簇为UGID:258077;进行后续EST组装、延伸和contigs序列校对,同时预测基因组同源性编码区和开放阅读框,并设计相应的引物。
结论：生物信息数据挖掘新基因克隆和功能预测平台能有效的发现组织特异性Unigene同源簇,为未知新基因的克隆和功能预测提供了很好的方向。
第二部分：小鼠睾丸特异表达基因TSEG-3的克隆、组织定位及表达谱分析
目的：应用生物信息数据挖掘新基因克隆和功能预测平台,预测可能参与睾丸精子发生和男性不育相关睾丸基因,并通过实验验证预测的准确性。
方法：首先,利用数字差异显示工具Digital Differential Display检索与EST片段BY706707.1的同源簇,按前文提出的生物信息数据挖掘新基因克隆和功能预测流程,预测出一个新的编码区序列；其次,通过RT-PCR验证,证实其确实存在于成年小鼠睾丸；第三,通过T／A克隆,进行测序分析其与设计序列的一致性和同源性,验证了新基因预测平台的可靠程度；通过原位杂交分析分析TSEG.3表达的细胞类型,同时以用Northern blotting分析转录本大小和RT-PCR分析TSEG-3多器官和不同发育阶段表达谱序列。
结果：成功从小鼠睾丸组织中扩增出TSEG-3,测序结果与预测结果一致,将基因序列提交Genbank数据库,获得Genbank登录号：EU477370(GI：186892498),命名为睾丸特异表达基因3(TSEG-3)。原位杂交结果显示TSEG-3表达于精原细胞、少量精母细胞和精子细胞等,Northern Blotting印迹证实转录长度为1023bp左右,小鼠TSEG-3多组织表达谱分析显示TSEG-3特异地表达于睾丸组织,在出生后2月龄时达到表达峰值。
结论：TSEG-3是睾丸特异性表达基因,具有发育阶段特异性特征,可能参与小鼠生精过程,深入研究TSEG-3的功能有助于阐明精子发生机制和男性不育机制。
第三部分：小鼠睾丸特异表达基因TSEG-3生物信息学分析及TSEG-3多克隆抗体制备与鉴定
目的：应用生物信息数据挖掘工具,进行TSEG-3蛋白生物信息学分析,为进一步研究TSEG-3蛋白的功能提供必要方向。
方法：应用序列信息挖掘软件,分析TSEG-3蛋白质物理特性、疏水区域和亲水区、跨膜区、蛋白质二级结构、蛋白质三级结构、拓朴树分析、特异性磷酸化位点、抗原位点及肽段、亚细胞定位及TSEG-3蛋白功能注释；设计TSEG-3抗原多肽并合成多肽,制备抗TSEG-3蛋白血清,Western Blotting鉴定TSEG-3蛋白分子量大小。
结果：通过序列分析显示TSEG-3位于第17号染色体17A3.3区,有六个外显子组成。TSEG-3属于DFU634家族基因,在TSEG-3的5’侧翼区域有六个启动子序列；miRNA检索发现16个可能相关miRNA;并在TSEG.3第277AA.285AA预测出一个非稳定区段(disordered regions)序列为：QSLHEALFG；TSEG-3属于非跨膜蛋白,47.8%可能胞质的：21.7%定位于细胞核；理论等电点为7.15.吸光系数为23680；具有10个丝氨酸(Serine)位点,3个酪氨酸(Tyrosine)位点,在第211到221间存在一个PKC位点,第28氨基酸残基处为信号肽区,第65个氨基酸为一个精氨酸／赖氨酸前肽切割位点(Arg(R)／Lys(K))等；TSEG-3蛋白预测理论分子量为38,52976KD和Western Blotting检测结果一致；功能预测提示TSEG-3可能参与生殖细胞的分化和凋亡。
结论：生物信息数据挖掘工具为新基因TSEG-3功能研究提供了丰富的研究内容,极大减少了TSEG-3功能研究的盲目性,加快了基因功能鉴定的速度。
第四部分：过表达TSEG-3基因对小鼠精母细胞系GC-2 spd增殖、凋亡的影响
目的：探讨TSEG-3过表达对小鼠精母细胞系GC-2 spd(ts)的增殖和凋亡的影响。
方法：通过构建pEGFP-TSEG-3载体和体外培养小鼠精母细胞系GC-2 spd(ts),观察融合蛋白EGFP-TSEG-3的亚细胞定位；采用MTT法检测TSEG-3过表达对GC-2spd(ts)细胞增殖的影响；应用流式细胞仪分析TSEG-3过表达对GC-2 spd(ts)细胞周期的作用；AO/EB双重染色法、Hoechst 33258/PI双染、Annexin V/PI双染法和JC-1染色分析TSEG-3过表达对GC-2 spd(ts)细胞凋亡率的影响；Real-Time RT-PCR分析TSEG-3过表达对凋亡相关蛋白的影响。
结果：融合蛋白EGFP-TSEG-3定位于细胞核,MTT法结果提示TSEG-3过表达抑制GC-2 spd(ts)细胞增殖；TSEG-3过表达诱导GC-2 spd(ts)细胞出现G1和G2／M期捕获。TSEG-3过表达诱导GC-2 spd(ts)细胞凋亡,Real-Time RT-PCR显示TSEG-3过表达诱导Fas上调,同时Bcl-2/Bax比率下调,说明Fas-Fas通路和Bcl-2/Bax均参与TSEG-3过表达诱导GC-2 spd(ts)细胞凋亡。
结论：TSEG-3过表达抑制GC-2 spd(ts)细胞增殖,诱导GC-2 spd(ts)细胞凋亡,Fas/Fas通路和Bcl-2/Bax均参与TSEG-3过表达诱导GC-2 spd(ts)细胞凋亡途径。
第五部分：TSEG-3过表达诱导小鼠睾丸精原细胞凋亡及其睾丸疾病模型表达谱分析
目的：整体水平探讨TSEG-3过表达对小鼠睾丸精原细胞的影响,并分析其在睾丸疾病模型表达谱。
方法：制备in vivo-jetPEITM-pEGFP-TSEG-3聚合物：构建TSEG-3过表达模型,HE染色和Tunel分析TSEG-3过表达对小鼠生殖细胞的影响；同时构建手术隐睾模型,分析TSEG-3转录水平与细胞凋亡率的关系,分析温度对TSEG-3转录水平的影响；复制17β雌二醇诱导隐睾模型,检测17β雌二醇诱导隐睾模型中TSEG-3转录水平,明确17β雌二醇对TSEG-3转录的影响。
结果：荧光显微镜观察提示In Vivo JetPEITM是一种高效、可靠安全的DNA转染试剂,能有效的携带外源DNA入生殖细胞。TSEG-3过表达使睾丸组织生精小管官腔内细胞密度减少,生精小管官腔变薄,大量精原细胞及精母细胞缺失,未发现成熟精子。Tunel结果提示TSEG-3过表达可能诱导生精细胞的凋亡。手术隐睾模型结果显示：TSEG-3的转录激活与手术组小鼠睾丸内生精细胞凋亡率增加呈正相关关系,进一步提示TSEG-3可能参与睾丸组织生精过程中细胞凋亡过程。17β-雌二醇诱导隐睾模型原位杂交结果提示17β-雌二醇可能参与抑制不同阶段生殖细胞中TSEG-3的转录。
结论：TSEG-3过表达诱导小鼠睾丸生殖细胞凋亡,温度可能诱导TSEG-3的转录,17β-雌二醇可能参与抑制不同阶段生殖细胞中TSEG-3的转录。
PART I Establishing of novel gene prediction platform based on bioinformatics data mining
Objective:According to the latest development of bioinformatics databases and data mining techniques, to fully exploit the valuable information of the existing biological database and build the new technology platforms of cloning and functional prediction based on bioinformatics and data mining.
Method:Search dbEST database and download target sequence involved in the research. The analysis of the selected sequence will be alignmented using the program of Blast in mouse genomic. Download the clusters of Unigene and the splicing, extension and assembly of EST clusters were carried out using Biolign software. The assembled contigs of EST clusters are analyzed by Blast_N in mouse genomics. Coding region sequences (CDS) of the homologous sequences of the assembled contigs in Genome were predicted via Genscan program. ORF sequences of the CDS were predicted by DNAssist. Each primers for ORF were designed by Primer Premier 5.0.
Results:The cDNA library of Mm.5168 was searched by the digital differential display between RIKEN full-length enriched mouse cDNA library and Epididymis cDNA library. The sequences of Unigene UGID:258077 for Mm.5168 was download. Some new CDS was predicted by Genescan and the open reading frame for each CDS was analyzed by the DNAssist.
Conclusion:The new gene prediction platform based on bioinformatics and data mining is a effective tool in searching the novel unknown genes and predicting the function of novel gene and provides a good direction for study of the novel gene.
PARTⅡThe cloning, cellular location of mouse testis-specific gene 3 and the analysis of expression profiles for TSEG-3
Objective:To predict the novel testis genes involved in spermatogenesis and male infertility using the novel gene prediction platform based on bioinformatics data mining and accuracy of prediction will be verified by RT-PCR.
Method:the homology EST clusters of BY706707.1 was found by using Digital Differential Display (DDD). The CDS for a novel gene was predicted by the novel gene prediction platform based on bioinformatics data mining. We detected the expression of the novel gene by RT-PCR. The cDNA of the novel gene was cloned into pGM-T and The cDNA of the novel gene was sequenced. The cellular location of the novel gene was analyzed by in situ hybridization. The length of transcript for the novel gene TSEG-3 was verified by nothern blotting. The expression profiles of the novel gene was analyzed by RT-PCR.
Results:A novel gene was amplified from adult mouse testis and named TSEG-3. No sequences were consistent with the sequence of TSEG-3 in Genebank database. The sequence of the novel gene TSEG-3 was consistent with the sequence of prediction. Brown Sediment for Hybridization signal of TSEG-3 mRNA was found in spermatogonia, spermatocytes, and a small amount of sperm cells, etc. The length of transcript of TSEG-3 is 1023bp by northern blotting. The transcript of TSEG-3 was detected only in testis tissue and up to peak at postnatal 2 months.
Conclusion:TSEG-3 is a testis specifically expressed gene and specific devolopment stages gene. TSEG-3 may participate in spermategensis and the study of TSEG-3 maybe help to eluciate the molecular mechanism of male infertility.
PART III Bioinformatics analysis of mouse testis-specific gene 3, the production of TSEG-3 polyclonal antibody and identification of TSEG-3 protein.
Objective:To provide the necessary research directions for TSEG-3 and to further study the function of TSEG-3 protein, the bioinformatics analysis of the mouse testis-specific gene 3 was executed via the tool based on bioinformatics & data mining tools.
Method:Physical property, hydrophobic regions, hydrophilic regions, transmembrane region, protein secondary structure, protein structure, phylogenetic tree, specific phosphorylation sites, antigenic sites and the peptides, subcellular localization and TSEG-3 protein functional annotation was predicted using the sequences'data mining software. Results:The theoretical molecular weight of TSEG-3 protein was 38529.76 Dalton, and its theoretical isoelectric point was 7.15. A putative conserved domain DUF634 was detected between 110 and 310 residues of TSEG-3 by National Biological Information Center (NCBI) blast_p program. The phylogenetic tree and homology tree indicated that TSEG-3 had a closest genetic relationship with rat LOC294301, and close genetic relationships with human C6orf81, Canis LOC474886, Bos taurus LOC540812, and Equus caballus LOC100053594. The nucleic acid and amino acid sequences of TSEG-3 were 99% homologous to those of 4930511I11Rik. NetPhos 1.0 Server showed a protein kinase C (PKC) phosphorylation site at the 224th residue of TSEG-3 protein. A propeptide cleavage site was found at the 56th residue of TSEG-3 protein by ProPv.l.Ob program. A signal peptide site was noted at the 28th residue of TSEG-3 protein by SignalP 3.0 Server. The PSORTⅡprogram showed that TSEG-3 protein localized in cytoplasm (47.8% possibility) or nuclei (21.7% possibility). The prediction of the PFP Server indicated that TSEG-3 might participate in cell differentiation and induction of apoptosis.
Conclusion:The function study of TSEG-3 protein was provided in different directions using bioinformatics & data mining tools, greatly reducing the blindness of the TSEG-3 function study, accelerated the pace of the identification of the novel gene function.
PART IV Overexpression of TSEG-3 inhibited mouse spermatogonial cell lines GC-2 spd cell proliferation and induce apoptosis
Objective:To analyze the regulation of proliferation and apoptosis for mouse spermatogonial cell lines GC-2 spd(ts) cell post-overexpression of TSEG-3 in vitro.
Method:Firstly, to watch the subcellular of TSEG-3 protein, the plasmid pEGFP-TSEG-3 was constructed. The proliferation of GC-2 spd(ts) cell was evaluated by MTT post-overexpression of TSEG-3. The cell cycle of GC-2 spd(ts) cell was detected by FCM after overexpression of TSEG-3. Apoptosis ratio of GC-2 spd(ts) cell was analyzed by FCM via AO/EB double staining, Hoechst 33258/PI double staining, Annexin V/PI double staining and the change of Mitochondrial membrane potential was evaluated by FCM via JC-1 staining. The transcript of apoptosis pathway related genes was detected by real-time quantitative RT-PCR.
Results:EGFP-TSEG-3 fusion protein is located in the nuclear of cos-7,239 cell and GC-2 spd(ts) cell. The proliferation of GC-2 spd(ts) cell was inhibited post overexpression of TSEG-3 via MTT. G2 and G2/M of GC-2 spd(ts) cell was arrested by FCM via PI staining after overexpression of TSEG-3. Overexpression of TSEG-3 induced the apoptosis of GC-2 spd(ts) cell. The transcript of Fas was up-regulated and the ratio of Bcl-2/Bax was down-regulted post overexpression of TSEG-3.
Conclusion:Overexpression of TSEG-3 inhibits the proliferation of GC-2 spd (ts) cell, and induces the apoptosis of GC-2 spd (ts) cells. Fas/Fas pathway and Bcl-2/Bax were involved in the process that overexpression of TSEG-3 induced the apoptosis of GC-2 spd (ts).
PART V TSEG-3 overexpression induced mouse testis germ cell apoptosis in vivo and expression pattern analysis of TSEG-3 in different cryptorchidism models
Objective:To explore the changes of mouse testis germ cell via the overexpression of TSEG-3, analyze the relationship between TSEG-3 and apoptosis of mouse testis germ cell in vivo and detected the expression profiles in different cryptorchidism models.
Method:Firstly, the polymer of in vivo-jetPEI TM-pEGFP-TSEG-3 was prepared according to standard protocol. The histological changes and apoptosis cells was evaluated by HE staining and Tunel after 72 hours post TSEG-3 overexpression in mouse testis. Secondly, Surgical cryptorchidism model was constructed according to the previous procedure. The TSEG-3 transcription level in testis of surgical cryptorchidism model was detected by real-time RT-PCR. The relationship between germ cell apoptosis and testis of surgical cryptorchidism was analyzed via Tunel staining. Thirdly, the 17β-estradiol induced cryptorchidism model was build using previous method. The transcript of TSEG-3 in testis tissues was analyzed by in situ hybridzation and real-time RT-PCR.
Results:In Vivo JetPEITM is an efficient, reliable and safe transfection reagent for plasmid DNA via fluorescence microscope and can effectively carry exogenous DNA into the germ cells. The number and cell density of seminiferous tubules reduced in testis of TSEG-3 overexpression model. Seminiferous tubule in testis of TSEG-3 overexpression model is thinner than control group. There were the loss of spermatogonia and spermatocyte, and not any mature sperm. Tunel results suggest TSEG-3 overexpression may induce germ cell apoptosis. In surgical cryptorchidism model, the transcript of TSEG-3 was up-regulated and positive correlation with testicular germ cell apoptosis. These resluts showed TSEG-3 may be involved in the process of testicular germ cell apoptosis. But, results of in situ hybridization and real-time RT-PCR suggest 17β-estradiol inhibited the TSEG-3 transcription in different stages of germ cells.
Conclusion:TSEG-3 overexpression induced germ cells apoptosis in vivo. Temperature may induce TSEG-3 transcription and 17β-estradiol maybe inhibit the TSEG-3 transcription in different stages of germ cells.
引文
[1]de Kretser D M. Male infertility. Lancet,1997,349,787-90.
[2]Krausz C, and Giachini C. Genetic risk factors in male infertility. Arch Androl, 2007,53,125-33.
[3]Ferlin A, Arredi B, and Foresta C. Genetic causes of male infertility. Reprod Toxicol,2006,22,133-41.
[4]Poongothai J, Gopenath T S, and Manonayaki S. Genetics of human male infertility. Singapore Med J,2009,50,336-47.
[5]Oates R D. The genetic basis of male reproductive failure. Urol Clin North Am, 2008,35,257-70, ix.
[6]Oatley J M, and Brinster R L. Regulation of spermatogonial stem cell self-renewal in mammals. Annu Rev Cell Dev Biol,2008,24,263-86.
[7]Moore J H. Bioinformatics. J Cell Physiol,2007,213,365-9.
[8]Yu B. In silico gene discovery. Methods Mol Med,2008,141,1-22.
[9]Lee J K, Williams P D, and Cheon S. Data mining in genomics. Clin Lab Med, 2008,28,145-66, viii.
[10]Jason T.L. Wang M J Z, Hannu T.T. Toivonen and Dennis Shasha. Data Mining in Bioinformatics.2005.
[11]Gu C-H, Tong Q-S, Zeng F-Q, et al. Cloning and sequence analysis of TSEG-1, a novel gene specifically expressed in mouse testis. Yichuan,2008,30,352-358.
[12]Hsu H-H. advanced data mining techonolgies in bioinformatics.2006.
[1]Lee J K, Williams P D, and Cheon S. Data mining in genomics. Clin Lab Med, 2008,28,145-66, viii.
[2]Moore J H. Bioinformatics. J Cell Physiol,2007,213,365-9.
[3]Zaki M J, Karypis G, and Yang J. Data Mining in Bioinformatics (BIOKDD). Algorithms Mol Biol,2007,2,4.
[4]Frank E, Hall M, Trigg L, et al. Data mining in bioinformatics using Weka. Bioinformatics,2004,20,2479-81.
[5]Campagne F, and Skrabanek L. Mining expressed sequence tags identifies cancer markers of clinical interest. BMC Bioinformatics,2006,7,481.
[6]Yu B. Role of in silico tools in gene discovery. Mol Biotechnol,2009,41,296-306.
[7]Zerikly M, and Challis G L. Strategies for the discovery of new natural products by genome mining. Chembiochem,2009,10,625-33.
[8]Yang Y, Adelstein S J, and Kassis A I. Target discovery from data mining approaches. Drug Discov Today,2009,14,147-54.
[9]Aouacheria A, Navratil V, Barthelaix A, et al. Bioinformatic screening of human ESTs for differentially expressed genes in normal and tumor tissues. BMC Genomics,2006,7,94.
[10]Torto T A, Li S, Styer A, et al. EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen Phytophthora. Genome Res,2003,13,1675-85.
[11]Scheurle D, DeYoung M P, Binninger D M, et al. Cancer gene discovery using digital differential display. Cancer Res,2000,60,4037-43.
[12]Handl J, Knowles J, and Kell D B. Computational cluster validation in post-genomic data analysis. Bioinformatics,2005,21,3201-12.
[13]King R D, Wise P H, and Clare A. Confirmation of data mining based predictions of protein function. Bioinformatics,2004,20,1110-8.
[14]Zhou Y, Young J A, Santrosyan A, et al. In silico gene function prediction using ontology-based pattern identification. Bioinformatics,2005,21,1237-45.
[15]Ivanciuc O, Schein C H, and Braun W. Data mining of sequences and 3D structures of allergenic proteins. Bioinformatics,2002,18,1358-64.
[16]Boguski M S, Lowe T M, and Tolstoshev C M. dbEST--database for "expressed sequence tags". Nat Genet,1993,4,332-3.
[17]Olesen C, Hansen C, Bendsen E, et al. Identification of human candidate genes for male infertility by digital differential display. Mol Hum Reprod,2001,7,11-20.
[18]Jones C E, Baumann U, and Brown A L. Automated methods of predicting the function of biological sequences using GO and BLAST. BMC Bioinformatics,2005, 6,272.
[19]Korf I, Flicek P, Duan D, et al. Integrating genomic homology into gene structure prediction. Bioinformatics,2001,17 Suppl 1,S140-8.
[20]Gu C-H, Tong Q-S, Zeng F-Q, et al. Cloning and sequence analysis of TSEG-1, a novel gene specifically expressed in mouse testis. Yichuan,2008,30,352-358.
[21]Wang Z Y, Tong Q S, Zeng F Q, et al. [Cloning and expression of a novel mouse testis gene TSEG-2]. Zhonghua Nan Ke Xue,2009,15,99-105.
[22]Patterton H G, and Graves S. DNAssist, a C++ program for editing and analysis of nucleic acid and protein sequences on PC-compatible computers running Windows 95,98, NT4.0 or 2000. Biotechniques,2000,28,1192-7.
[23]TA H. BioEdit:a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser.,1999,41,95-98.
[24]Singh V K, Mangalam A K, Dwivedi S, et al. Primer premier:program for design of degenerate primers from a protein sequence. Biotechniques,1998,24,318-9.
[25]Swindells M, Rae M, Pearce M, et al. Application of high-throughput computing in bioinformatics. Philos Transact A Math Phys Eng Sci,2002,360,1179-89.
[26]Park J, Hu Y, Murthy T V, et al. Building a human kinase gene repository: bioinformatics, molecular cloning, and functional validation. Proc Natl Acad Sci U S A,2005,102,8114-9.
[27]Li X, Rao S, Wang Y, et al. Gene mining:a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Res,2004,32,2685-94.
[28]Martinez R, Pasquier N, and Pasquier C. GenMiner:mining non-redundant association rules from integrated gene expression data and annotations. Bioinformatics,2008,24,2643-4.
[29]Gotz S, Garcia-Gomez J M, Terol J, et al. High-throughput functional annotation and data mining with the Blast2G0 suite. Nucleic Acids Res,2008,36,3420-35.
[30]Okuno Y. [In silico drug discovery based on the integration of bioinformatics and chemoinformtics]. Yakugaku Zasshi,2008,128,1645-51.
[31]Alves R, and Sorribas A. In silico pathway reconstruction:Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae. BMC Syst Biol,2007,1,10.
[32]Ekins S, Mestres J, and Testa B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol,2007,152,21-37.
[33]Bertone P, and Gerstein M. Integrative data mining:the new direction in bioinformatics. IEEE Eng Med Biol Mag,2001,20,33-40.
[34]Wan J S, Sharp S J, Poirier G M, et al. Cloning differentially expressed mRNAs. Nat Biotechnol,1996,14,1685-91.
[35]De Young M P, Damania H, Scheurle D, et al. Bioinformatics-based discovery of a novel factor with apparent specificity to colon cancer. In Vivo,2002,16,239-48.
[36]Kumar D, Cousin C, Tian S, et al. Identification of differentially expressed genes in pancreatic cancer using digital differential display. Proceedings of the American Association for Cancer Research Annual Meeting,2004,45,395.
[37]Cutler S, and Somerville C. Cloning in silico. Curr Biol,1997,7,R108-11.
[38]Hosking L, Gill R, and Sanseau P. Rapid in silico cloning of homologous genes using a new biotool:ESTBlast. Journal of Medical Genetics,1997,34,S27.
[39]Brazma A, Jonassen I, Vilo J, et al. Predicting gene regulatory elements in silico on a genomic scale. Genome Res,1998,8,1202-15.
[40]Segovia L. Getting closer to efficient gene discovery, in silico. Nat Biotechnol, 1998,16,25.
[41]Wei M H, Karavanova I, Ivanov S V, et al. In silico-initiated cloning and molecular characterization of a novel human member of the L1 gene family of neural cell adhesion molecules. Hum Genet,1998,103,355-64.
[42]Gu C H, Tong Q S, Zeng F Q, et al. Cloning and sequence analysis of TSEG-1, a novel gene specifically expressed in mouse testis. Yi Chuan,2008,30,352-8.
[43]Gu C, Tong Q, Zheng L, et al. TSEG-1, a novel member of histone H2A variants, participates in spematogenesis via promoting apoptosis of spermatogenic cells. Genomics,2010,95,278-89.
[1]de Kretser D M. Male infertility. Lancet,1997,349,787-90.
[2]Wieacker P, and Jakubiczka S. Genetic causes of male infertility. Andrologia,1997, 29,63-9.
[3]Ferlin A, Arredi B, and Foresta C. Genetic causes of male infertility. Reprod Toxicol,2006,22,133-41.
[4]Krausz C, and Giachini C. Genetic risk factors in male infertility. Arch Androl, 2007,53,125-33.
[5]Koji T, and Hishikawa Y. Germ cell apoptosis and its molecular trigger in mouse testes. Arch Histol Cytol,2003,66,1-16.
[6]Ferlin A, Raicu F, Gatta V, et al. Male infertility:role of genetic background. Reprod Biomed Online,2007,14,734-45.
[7]De Kretser D M, and Baker H W. Infertility in men:recent advances and continuing controversies. J Clin Endocrinol Metab,1999,84,3443-50.
[8]DeJong J. Basic mechanisms for the control of germ cell gene expression. Gene, 2006,366,39-50.
[9]O'Donnell L, Robertson K M, Jones M E, et al. Estrogen and spermatogenesis. Endocr Rev,2001,22,289-318.
[10]Lin Y N, and Matzuk M M. High-throughput discovery of germ-cell-specific genes. Semin Reprod Med,2005,23,201-12.
[11]Meade J D, Cho Y J, Shester B R, et al. Automated fluorescent differential display for cancer gene profiling. Methods Mol Biol,2010,576,99-133.
[12]Mizuno K, Kojima Y, Kurokawa S, et al. Identification of differentially expressed genes in human cryptorchid testes using suppression subtractive hybridization. J Urol,2009,181,1330-7; discussion 1337.
[13]Beissbarth T, Borisevich I, Horlein A, et al. Analysis of CREM-dependent gene expression during mouse spermatogenesis. Mol Cell Endocrinol,2003,212,29-39.
[14]Yu B. Role of in silico tools in gene discovery. Mol Biotechnol,2009,41,296-306.
[1] Sobhani K. Urine proteomic analysis: use of two-dimensional gel electrophoresis, isotope coded affinity tags, and capillary electrophoresis. Methods Mol Biol, 2010, 641,325-46.
[2] Choi H S, Lee S H, Kim H, et al. Germ cell-specific gene 1 targets testis-specific poly(A) polymerase to the endoplasmic reticulum through protein-protein interactions. FEBS Lett, 2008, 582,1203-9.
[3] Chen Y, and Xu D. Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae. Nucleic Acids Res, 2004, 32,6414-24.
[4] Golebiowski F, Tatham M H, Nakamura A, et al. High-stringency tandem affinity purification of proteins conjugated to ubiquitin-like moieties. Nat Protoc, 2010, 5,873-82.
[5] Paulucci-Holthauzen A A, Vergara L A, Bellot L J, et al. Spatial Distribution of Protein Kinase A Activity during Cell Migration Is Mediated by A-kinase Anchoring Protein AKAP Lbc. J Biol Chem, 2009, 284,5956-67.
[6] Chen T, Wang J, Xing D, et al. Spatio-temporal dynamic analysis of bid activation and apoptosis induced by alkaline condition in human lung adenocarcinoma cell. Cell Physiol Biochem, 2007, 20,569-78.
[7] Kang N, Duan L, Tang L, et al. Identification and characterization of a novel thymus aging related protein Rwddl. Cell Mol Immunol, 2008, 5,279-85.
[8] Babnigg G, and Joachimiak A. Predicting protein crystallization propensity from protein sequence. J Struct Funct Genomics, 2010, 11,71-80.
[9] Yoon S, and De Micheli G. Computational identification of microRNAs and their targets. Birth Defects Res C Embryo Today, 2006, 78,118-28.
[10] Ferlin A, Raicu F, Gatta V, et al. Male infertility: role of genetic background. Reprod Biomed Online, 2007, 14,734-45.
[11] Wang J, Ungar L H, Tseng H, et al. MetaProm: a neural network based meta-predictor for alternative human promoter prediction. BMC Genomics, 2007, 8,374.
[12] Chen C B, and Li T. A hybrid neural network system for prediction and recognition
of promoter regions in human genome. J Zhejiang Univ Sci B, 2005, 6,401-7.
[13] Keshavan R, Virata M, Keshavan A, et al. Computational identification of Ciona intestinalis microRNAs. Zoolog Sci, 2010, 27,162-70.
[14] Giampietri C, Petrungaro S, Coluccia P, et al. Germ cell apoptosis control during spermatogenesis. Contraception, 2005, 72,298-302.
[15] Ferlin A, Arredi B, and Foresta C. Genetic causes of male infertility. Reprod Toxicol, 2006, 22,133-41.
[16] Cooper H M, and Paterson Y. Production of polyclonal antisera. Curr Protoc Cytom, 2008, Appendix 3,Appendix 31.
[17] Cooper H M, and Paterson Y. Production of polyclonal antisera. Curr Protoc Neurosci, 2009, Chapter 5,Unit 5 5.
[1]Frericks M, and Esser C. A toolbox of novel murine house-keeping genes identified by meta-analysis of large scale gene expression profiles. Biochim Biophys Acta, 2008,1779.830-7.
[2]Wolkowicz M J, Coonrod S A. Reddi P P. et al. Refinement of the differentiated phenotype of the spermatogenic cell line GC-2 spd(ts). Biol Reprod,1996, 55,923-32.
[3]Hofmann M C. Narisawa S, Hess R A, et al. Immortalization of germ cells and somatic testicular cells using the SV40 large T antigen. Experimental Cell Research, 1992,201,417-35.
[4]Tsien R Y. The green fluorescent protein. Annu Rev Biochem,1998,67,509-44.
[5]March J C, Rao G, and Bentley W E. Biotechnological applications of green fluorescent protein. Appl Microbiol Biotechnol,2003,62,303-15.
[6]Yeh E, Gustafson K, and Boulianne G L. Green fluorescent protein as a vital marker and reporter of gene expression in Drosophila. Proc Natl Acad Sci U S A, 1995,92,7036-40.
[7]Chalfie M, Tu Y, Euskirchen G, et al. Green fluorescent protein as a marker for gene expression. Science,1994,263,802-5.
[8]Dan W B, Zhang C, Guan Z B, et al. Construction of bifunctional fusion proteins consisting of duck BAFF and EGFP. Biotechnol Lett,2008,30,221-7.
[9]Dai L C, Xu D Y, Yao X, et al. Construction of a fusion protein expression vector MK-EGFP and its subcellular localization in different carcinoma cell lines. World J Gastroenterol,2006,12,7649-53.
[10]Cao P, Zhang S, Zhang J, et al. Construction and characterization of a bi-functional EGFP/sBAFF fusion protein. Biochimie,2006,88,629-35.
[11]Zindy F, den Besten W, Chen B, et al. Control of spermatogenesis in mice by the cyclin D-dependent kinase inhibitors pl8(Ink4c) and pl9(Ink4d). Molecular and Cellular Biology,2001,21,3244-55.
[12]Lange C, Huttner W B, and Calegari F. Cdk4/cyclinD1 overexpression in neural stem cells shortens G1, delays neurogenesis, and promotes the generation and expansion of basal progenitors. Cell Stem Cell,2009,5,320-31.
[13]Zhou Z M, Sha J H, Li J M, et al. Expression of a novel reticulon-like gene in human testis. Reproduction,2002,123,227-34.
[14]Yazama F, Furuta K, Fujimoto M, et al. Abnormal spermatogenesis in mice unable to synthesize ascorbic acid. Anat Sci Int,2006,81,115-25.
[15]Namiki M, Koide T, Okuyama A, et al. Abnormality of testicular FSH receptors in infertile men. Acta Endocrinol (Copenh),1984,106,548-55.
[16]Diemer T, and Desjardins C. Developmental and genetic disorders in spermatogenesis. Hum Reprod Update,1999,5,120-40.
[17]Kim H H, and Schlegel P N. Endocrine manipulation in male infertility. Urol Clin North Am,2008,35,303-18, x.
[18]Scott L S. Fertility in cryptorchidism. Proc R Soc Med,1962,55,1047-50.
[19]Maclean J A,2nd, and Wilkinson M F. Gene regulation in spermatogenesis. Curr Top Dev Biol, 2005,71,131-97.
[20]Koji T, Izumi S, Tanno M, et al. Localization in situ of c-myc mRNA and c-myc protein in adult mouse testis. Histochem J,1988,20,551-7.
[21]Kotaja N, De Cesare D, Macho B, et al. Abnormal sperm in mice with targeted deletion of the act (activator of cAMP-responsive element modulator in testis) gene. Proc Natl Acad Sci U S A,2004,101,10620-5.
[22]Fan G C, Ren X, Qian J, et al. Novel cardioprotective role of a small heat-shock protein, Hsp20, against ischemia/reperfusion injury. Circulation,2005,111,1792-9.
[23]Fujisawa M, Shirakawa T, Fujioka H, et al. Adenovirus-mediated p53 gene transfer to rat testis impairs spermatogenesis. Arch Androl,2001,46,223-31.
[24]Vicini E, Loiarro M, Di Agostino S, et al.17-beta-estradiol elicits genomic and non-genomic responses in mouse male germ cells. J Cell Physiol,2006, 206,238-45.
[25]Beumer T L, Roepers-Gajadien H L, Gademan I S, et al. Apoptosis regulation in the testis:involvement of Bcl-2 family members. Mol Reprod Dev,2000,56,353-9.
[26]Grobholz R, Zentgraf H, Kohrmann K U, et al. Bax, Bcl-2, fas and Fas-L antigen expression in human seminoma:correlation with the apoptotic index. APMIS,2002, 110,724-32.
[27]Russell L D, Chiarini-Garcia H, Korsmeyer S J, et al. Bax-dependent spermatogonia apoptosis is required for testicular development and spermatogenesis. Biol Reprod,2002,66,950-8.
[28]Moreno R D, Lizama C, Urzua N, et al. Caspase activation throughout the first wave of spermatogenesis in the rat. Cell Tissue Res,2006,325,533-40.
[1]Pettersson A, Richiardi L, Nordenskjold A, et al. Age at surgery for undescended testis and risk of testicular cancer. N Engl J Med,2007,356,1835-41.
[2]Kaleva M, and Toppari J. Genetics and hormones in testicular descent. Hormones (Athens),2003,2,211-6.
[3]Rossato M, Tavolini I M, Calcagno A, et al. The novel hormone INSL3 is expressed in human testicular Leydig cell tumors:A clinical and immunohistochemical study. Urol Oncol,2008,
[4]Hafizur R M, Yano M, Gotoh T, et al. Modulation of chaperone activities of Hsp70 and Hsp70-2 by a mammalian DnaJ/Hsp40 homolog, DjA4. J Biochem,2004, 135,193-200.
[5]Krawczyk Z, Wisniewski J, and Biesiada E. A rat testis-specific hsp70 gene-related transcript is coded by a novel gene from the hsp70 multigene family. Acta Biochim Pol,1988,35,377-85.
[6]Sarge K D, and Cullen K E. Regulation of hsp expression during rodent spermatogenesis. Cell Mol Life Sci,1997,53,191-7.
[7]Hughes I A, and Acerini C L. Factors controlling testis descent. Eur J Endocrinol, 2008,159 Suppl 1,S75-82.
[8]Cartier R, Ren S V, Walther W, et al. In vivo gene transfer by low-volume jet injection. Anal Biochem,2000,282,262-5.
[9]Li E Z, Li D X, Zhang S Q, et al.17beta-estradiol stimulates proliferation of spermatogonia in experimental cryptorchid mice. Asian J Androl,2007,9,659-67.
[10]Xu J, Xu Z, Jiang Y, et al. Cryptorchidism induces mouse testicular germ cell apoptosis and changes in bcl-2 and bax protein expression. J Environ Pathol Toxicol Oncol,2000,19,25-33.
[11]Dalby B, Cates S, Harris A, et al. Advanced transfection with Lipofectamine 2000 reagent:primary neurons, siRNA, and high-throughput applications. Methods,2004, 33,95-103.
[12]Clements B A. Incani V, Kucharski C, et al. A comparative evaluation of poly-L-lysine-palmitic acid and Lipofectamine 2000 for plasmid delivery to bone marrow stromal cells. Biomaterials,2007,28,4693-704.
[13]Zhang Q, Cheng S X, Zhang X Z. et al. Water soluble polymer protected lipofectamine 2000/DNA complexes for solid-phase transfection. Macromol Biosci, 2009,9,1262-71.
[14]Bosco A P, Rhem R G, and Dolovich M B. In vitro estimations of in vivo jet nebulizer efficiency using actual and simulated tidal breathing patterns. J Aerosol Med,2005,18,427-38.
[15]Sawamura D, Ina S, Itai K, et al. In vivo gene introduction into keratinocytes using jet injection. Gene Ther,1999,6,1785-7.
[16]Walther W, Stein U, Fichtner I, et al. Nonviral in vivo gene delivery into tumors using a novel low volume jet-injection technology. Gene Ther,2001,8,173-80.
[17]Walther W, Stein U, Fichtner I, et al. Nonviral jet-injection gene transfer for efficient in vivo cytosine deaminase suicide gene therapy of colon carcinoma. Mol Ther,2005,12,1176-84.
[18]Walther W, Fichtner I, Schlag P M, et al. Nonviral jet-injection technology for intratumoral in vivo gene transfer of naked DNA. Methods Mol Biol,2009, 542,195-208.
[19]Giampietri C, Petrungaro S, Coluccia P, et al. Germ cell apoptosis control during spermatogenesis. Contraception,2005,72,298-302.
[20]Boekelheide K. Mechanisms of toxic damage to spermatogenesis. J Natl Cancer Inst Monogr,2005,6-8.
[21]Koji T, and Hishikawa Y. Germ cell apoptosis and its molecular trigger in mouse testes. Arch Histol Cytol,2003,66,1-16.
[22]Lecoeur H. Nuclear apoptosis detection by flow cytometry:influence of endogenous endonucleases. Exp Cell Res,2002,277,1-14.
[23]Mauduit C, Hamamah S, and Benahmed M. Stem cell factor/c-kit system in spermatogenesis. Hum Reprod Update,1999,5,535-45.
[24]Ferlin A, Raicu F, Gatta V, et al. Male infertility:role of genetic background. Reprod Biomed Online,2007,14,734-45.
[25]Rodriguez I, Ody C, Araki K, et al. An early and massive wave of germinal cell apoptosis is required for the development of functional spermatogenesis. EMBO J, 1997,16,2262-70.
[1]Moore JH. Bioinformatics. J Cell Physiol,2007,213:365-369.
[2]Wang JTL, Zaki MJ, Toivonen HTT, et al. Data Mining in Bioinformatics (Advanced information and knowledge processing).1st Edition. London, Springer-Verlag London Limited 2005.1-38.
[3]HU X, PAN Y. Knowledge Discovery In Bioinformatics:Techniques, Methods, and Applications. 1st Edition. Canada, Wiley interscience 2007.1-404.
[4]Hsu HH. Advanced Data Mining Technologies In Bioinformatics. Taiwan, Idea group publishing 2006:1-343.
[5]Lee JK, Williams PD, Cheon S. Data mining in genomics. Clin Lab Med,2008,28:145-166, viii.
[6]Zaki MJ, Karypis G, Yang J. Data Mining in Bioinformatics (BIOKDD). Algorithms Mol Biol, 2007,2:4.
[7]Bertone P, Gerstein M. Integrative data mining:the new direction in bioinformatics. IEEE Eng Med Biol Mag,2001,20:33-40.
[8]Cakmak A, Ozsoyoglu G. Mining biological networks for unknown pathways. Bioinformatics, 2007,23:2775-2783.
[9]Yang ZR, Hamer R. Bio-basis function neural networks in protein data mining. Curr Pharm Des, 2007,13:1403-1413.
[10]Prather JC, Lobach DF, Goodwin LK, et al. Medical data mining:knowledge discovery in a clinical data warehouse. Proc AMIA Annu Fall Symp,1997:101-105.
[11]Miyagawa T, Nishida N, Ohashi J, et al. Appropriate data cleaning methods for genome-wide association study. J Hum Genet,2008,53:886-893.
[12]Roberts BL, Anthony MK, Madigan EA, et al. Data management:cleaning and checking. Nurs Res, 1997,46:350-352.
[13]Herrero J, Diaz-Uriarte R, Dopazo J. Gene expression data preprocessing. Bioinformatics,2003, 19:655-656.
[14]Shinde K, Phatak M, Johannes FM, et al. Genomics Portals:integrative web-platform for mining genomics data. BMC Genomics,2010,11:27.
[15]Chen H, Ding L, Wu Z, et al. Semantic web for integrated network analysis in biomedicine. Brief Bioinform,2009,10:177-192.
[16]Baitaluk M, Ponomarenko J. Semantic Integration of Data on Transcriptional Regulation. Bioinformatics,2010.
[17]Lenz R, Beyer M, Kuhn KA. Semantic integration in healthcare networks. Int J Med Inform,2007, 76:201-207.
[18]Martin L, Anguita A, de la Calle G, et al. Semantic data integration in the European ACGT project. AMIA Annu Symp Proc,2007:1042.
[19]Regad L, Martin J, Nuel G, et al. Mining protein loops using a structural alphabet and statistical exceptionality. BMC Bioinformatics,2010,11:75.
[20]Zheng H, Chruszcz M, Lasota P, et al. Data mining of metal ion environments present in protein structures. J Inorg Biochem,2008,102:1765-1776.
[21]Exarchos TP, Papaloukas C, Lampros C, et al. Protein classification using sequential pattern mining. Conf Proc IEEE Eng Med Biol Soc,2006,1:5814-5817.
[22]Dobson PD, Cai YD, Stapley BJ, et al. Prediction of protein function in the absence of significant sequence similarity. Curr Med Chem,2004,11:2135-2142.
[23]Vinga S, Gouveia-Oliveira R, Almeida JS. Comparative evaluation of word composition distances for the recognition of SCOP relationships. Bioinformatics,2004,20:206-215.
[24]Hohl M, Ragan MA. Is multiple-sequence alignment required for accurate inference of phylogeny? Syst Biol,2007,56:206-221.
[25]Daraselia N, Yuryev A, Egorov S, et al. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks. BMC Bioinformatics,2007,8:243.
[26]Bhattacharya A, De RK. Bi-correlation clustering algorithm for determining a set of co-regulated genes. Bioinformatics,2009,25:2795-2801.
[27]Bales ME, Johnson SB. Graph theoretic modeling of large-scale semantic networks. J Biomed Inform,2006,39:451-464.
[28]Tseng GC. Quantile map:simultaneous visualization of patterns in many distributions with application to tandem mass spectrometry. Comput Stat Data Anal,2010,54:1124.
[29]Jupiter DC, VanBuren V. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS One,2008,3:e1717.
[30]Curk T, Demsar J, Xu Q, et al. Microarray data mining with visual programming. Bioinformatics, 2005,21:396-398.
[31]Wegman EJ. Visual data mining. Stat Med,2003,22:1383-1397.
[32]Numao N, Kamimoto Y. Sequence Fourier analysis of a specific protein-DNA (RNA) interaction; an intermolecular frequency symmetry. Chem Pharm Bull (Tokyo).2009,57:1305-1307.
[33]Yin C, Yau SS. Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence. J Theor Biol,2007,247:687-694.
[34]Bulyk ML. Analysis of sequence specificities of DNA-binding proteins with protein binding microarrays. Methods Enzymol,2006,410:279-299.
[35]Rehm BH. Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification. Appl Microbiol Biotechnol,2001,57:579-592.
[36]Menashe I, Maeder D, Garcia-Closas M, et al. Pathway Analysis of Breast Cancer Genome-Wide Association Study Highlights Three Pathways and One Canonical Signaling Cascade. Cancer Res, 2010.
[37]Luo L, Peng G, Zhu Y, et al. Genome-wide gene and pathway analysis. Eur J Hum Genet,2010.
[38]Clemmensen A, Andersen KE, Clemmensen O, et al. Genome-Wide Expression Analysis of Human In Vivo Irritated Epidermis:Differential Profiles Induced by Sodium Lauryl Sulfate and Nonanoic Acid. J Invest Dermatol,2010.
[39]Kim S, Webster MJ. Integrative genome-wide association analysis of cytoarchitectural abnormalities in the prefrontal cortex of psychiatric disorders. Mol Psychiatry,2010.
[40]Lesch KP, Selch S, Renner TJ, et al. Genome-wide copy number variation analysis in attention-deficit/hyperactivity disorder:association with neuropeptide Y gene dosage in an extended pedigree. Mol Psychiatry,2010.
[41]Zhang L, Guo YF, Liu YZ, et al. Pathway-based genome-wide association analysis identified the importance of regulation-of-autophagy pathway for ultradistal radius BMD. J Bone Miner Res, 2010.
[42]Rollins B, Martin MV, Morgan L, et al. Analysis of whole genome biomarker expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet,2010.
[43]Bouzigon E. Forabosco P, Koppelman GH, et al. Meta-analysis of 20 genome-wide linkage studies evidenced new regions linked to asthma and atopy. Eur J Hum Genet,2010.
[44]Ivanov AS, Dubanov AV, Skvortsov VS, et al. [Computer drug design based on analysis of a target macromolecule structure. I. Search and description of a ligand binding site in a target molecule]. Vopr Med Khim,2002,48:304-315.
[45]Doherty RS, De Oliveira T, Seebregts C. et al. BioAfrica's HIV-1 proteomics resource:combining protein data with bioinformatics tools. Retrovirology,2005,2:18.
[46]Pattabiraman N. Analysis of ligand-macromolecule contacts:computational methods. Curr Med Chem,2002,9:609-621.
[47]Shen K, Tseng GC. Meta-analysis for pathway enrichment analysis when combining multiple genomic studies. Bioinformatics,2010,26:1316-1323.
[48]Arum CJ, Anderssen E, Tommeras K, et al. Gene expression profiling and pathway analysis of superficial bladder cancer in rats. Urology,2010,75:742-749.
[49]Zamar D, Tripp B, Ellis G, et al. Path:a tool to facilitate pathway-based genetic association analysis. Bioinformatics,2009,25:2444-2446.
[50]Elbers CC, van Eijk KR, Franke L, et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol,2009,33:419-431.
[51]Hu ZZ, Huang H, Cheema A, et al. Integrated Bioinformatics for Radiation-Induced Pathway Analysis from Proteomics and Microarray Data. J Proteomics Bioinform,2008,1:47-60.
[52]Zhou X, Wong ST. Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening. Proc IEEE Inst Electr Electron Eng,2008,96:1310-1331.
[53]Werner T. Bioinformatics applications for pathway analysis of microarray data. Curr Opin Biotechnol,2008,19:50-54.
[54][54] Zhang Y, Szustakowski J, Schinke M. Bioinformatics analysis of microarray data. Methods Mol Biol,2009,573:259-284.
[55]Rasnick D. DATE analysis:A general theory of biological change applied to microarray data. Biotechnol Prog,2009,25:1275-1288.
[56]Xia XQ, McClelland M, Porwollik S, et al. WebArrayDB:cross-platform microarray data analysis and public data repository. Bioinformatics,2009,25:2425-2429.
[57]Hauser PV, Perco P, Muhlberger I, et al. Microarray and bioinformatics analysis of gene expression in experimental membranous nephropathy. Nephron Exp Nephrol,2009,112:e43-58.
[58]Li C. Automating dChip:toward reproducible sharing of microarray data analysis. BMC Bioinformatics,2008,9:231.
[59]Moller-Levet CS, West CM, Miller CJ. Exploiting sample variability to enhance multivariate analysis of microarray data. Bioinformatics,2007,23:2733-2740.
[2]Krausz C, and Giachini C. Genetic risk factors in male infertility. Arch Androl, 2007,53,125-33.
[3]Ferlin A, Arredi B, and Foresta C. Genetic causes of male infertility. Reprod Toxicol,2006,22,133-41.
[4]Poongothai J, Gopenath T S, and Manonayaki S. Genetics of human male infertility. Singapore Med J,2009,50,336-47.
[5]Oates R D. The genetic basis of male reproductive failure. Urol Clin North Am, 2008,35,257-70, ix.
[6]Oatley J M, and Brinster R L. Regulation of spermatogonial stem cell self-renewal in mammals. Annu Rev Cell Dev Biol,2008,24,263-86.
[7]Moore J H. Bioinformatics. J Cell Physiol,2007,213,365-9.
[8]Yu B. In silico gene discovery. Methods Mol Med,2008,141,1-22.
[9]Lee J K, Williams P D, and Cheon S. Data mining in genomics. Clin Lab Med, 2008,28,145-66, viii.
[10]Jason T.L. Wang M J Z, Hannu T.T. Toivonen and Dennis Shasha. Data Mining in Bioinformatics.2005.
[11]Gu C-H, Tong Q-S, Zeng F-Q, et al. Cloning and sequence analysis of TSEG-1, a novel gene specifically expressed in mouse testis. Yichuan,2008,30,352-358.
[12]Hsu H-H. advanced data mining techonolgies in bioinformatics.2006.
[1]Lee J K, Williams P D, and Cheon S. Data mining in genomics. Clin Lab Med, 2008,28,145-66, viii.
[2]Moore J H. Bioinformatics. J Cell Physiol,2007,213,365-9.
[3]Zaki M J, Karypis G, and Yang J. Data Mining in Bioinformatics (BIOKDD). Algorithms Mol Biol,2007,2,4.
[4]Frank E, Hall M, Trigg L, et al. Data mining in bioinformatics using Weka. Bioinformatics,2004,20,2479-81.
[5]Campagne F, and Skrabanek L. Mining expressed sequence tags identifies cancer markers of clinical interest. BMC Bioinformatics,2006,7,481.
[6]Yu B. Role of in silico tools in gene discovery. Mol Biotechnol,2009,41,296-306.
[7]Zerikly M, and Challis G L. Strategies for the discovery of new natural products by genome mining. Chembiochem,2009,10,625-33.
[8]Yang Y, Adelstein S J, and Kassis A I. Target discovery from data mining approaches. Drug Discov Today,2009,14,147-54.
[9]Aouacheria A, Navratil V, Barthelaix A, et al. Bioinformatic screening of human ESTs for differentially expressed genes in normal and tumor tissues. BMC Genomics,2006,7,94.
[10]Torto T A, Li S, Styer A, et al. EST mining and functional expression assays identify extracellular effector proteins from the plant pathogen Phytophthora. Genome Res,2003,13,1675-85.
[11]Scheurle D, DeYoung M P, Binninger D M, et al. Cancer gene discovery using digital differential display. Cancer Res,2000,60,4037-43.
[12]Handl J, Knowles J, and Kell D B. Computational cluster validation in post-genomic data analysis. Bioinformatics,2005,21,3201-12.
[13]King R D, Wise P H, and Clare A. Confirmation of data mining based predictions of protein function. Bioinformatics,2004,20,1110-8.
[14]Zhou Y, Young J A, Santrosyan A, et al. In silico gene function prediction using ontology-based pattern identification. Bioinformatics,2005,21,1237-45.
[15]Ivanciuc O, Schein C H, and Braun W. Data mining of sequences and 3D structures of allergenic proteins. Bioinformatics,2002,18,1358-64.
[16]Boguski M S, Lowe T M, and Tolstoshev C M. dbEST--database for "expressed sequence tags". Nat Genet,1993,4,332-3.
[17]Olesen C, Hansen C, Bendsen E, et al. Identification of human candidate genes for male infertility by digital differential display. Mol Hum Reprod,2001,7,11-20.
[18]Jones C E, Baumann U, and Brown A L. Automated methods of predicting the function of biological sequences using GO and BLAST. BMC Bioinformatics,2005, 6,272.
[19]Korf I, Flicek P, Duan D, et al. Integrating genomic homology into gene structure prediction. Bioinformatics,2001,17 Suppl 1,S140-8.
[20]Gu C-H, Tong Q-S, Zeng F-Q, et al. Cloning and sequence analysis of TSEG-1, a novel gene specifically expressed in mouse testis. Yichuan,2008,30,352-358.
[21]Wang Z Y, Tong Q S, Zeng F Q, et al. [Cloning and expression of a novel mouse testis gene TSEG-2]. Zhonghua Nan Ke Xue,2009,15,99-105.
[22]Patterton H G, and Graves S. DNAssist, a C++ program for editing and analysis of nucleic acid and protein sequences on PC-compatible computers running Windows 95,98, NT4.0 or 2000. Biotechniques,2000,28,1192-7.
[23]TA H. BioEdit:a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser.,1999,41,95-98.
[24]Singh V K, Mangalam A K, Dwivedi S, et al. Primer premier:program for design of degenerate primers from a protein sequence. Biotechniques,1998,24,318-9.
[25]Swindells M, Rae M, Pearce M, et al. Application of high-throughput computing in bioinformatics. Philos Transact A Math Phys Eng Sci,2002,360,1179-89.
[26]Park J, Hu Y, Murthy T V, et al. Building a human kinase gene repository: bioinformatics, molecular cloning, and functional validation. Proc Natl Acad Sci U S A,2005,102,8114-9.
[27]Li X, Rao S, Wang Y, et al. Gene mining:a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Res,2004,32,2685-94.
[28]Martinez R, Pasquier N, and Pasquier C. GenMiner:mining non-redundant association rules from integrated gene expression data and annotations. Bioinformatics,2008,24,2643-4.
[29]Gotz S, Garcia-Gomez J M, Terol J, et al. High-throughput functional annotation and data mining with the Blast2G0 suite. Nucleic Acids Res,2008,36,3420-35.
[30]Okuno Y. [In silico drug discovery based on the integration of bioinformatics and chemoinformtics]. Yakugaku Zasshi,2008,128,1645-51.
[31]Alves R, and Sorribas A. In silico pathway reconstruction:Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae. BMC Syst Biol,2007,1,10.
[32]Ekins S, Mestres J, and Testa B. In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol,2007,152,21-37.
[33]Bertone P, and Gerstein M. Integrative data mining:the new direction in bioinformatics. IEEE Eng Med Biol Mag,2001,20,33-40.
[34]Wan J S, Sharp S J, Poirier G M, et al. Cloning differentially expressed mRNAs. Nat Biotechnol,1996,14,1685-91.
[35]De Young M P, Damania H, Scheurle D, et al. Bioinformatics-based discovery of a novel factor with apparent specificity to colon cancer. In Vivo,2002,16,239-48.
[36]Kumar D, Cousin C, Tian S, et al. Identification of differentially expressed genes in pancreatic cancer using digital differential display. Proceedings of the American Association for Cancer Research Annual Meeting,2004,45,395.
[37]Cutler S, and Somerville C. Cloning in silico. Curr Biol,1997,7,R108-11.
[38]Hosking L, Gill R, and Sanseau P. Rapid in silico cloning of homologous genes using a new biotool:ESTBlast. Journal of Medical Genetics,1997,34,S27.
[39]Brazma A, Jonassen I, Vilo J, et al. Predicting gene regulatory elements in silico on a genomic scale. Genome Res,1998,8,1202-15.
[40]Segovia L. Getting closer to efficient gene discovery, in silico. Nat Biotechnol, 1998,16,25.
[41]Wei M H, Karavanova I, Ivanov S V, et al. In silico-initiated cloning and molecular characterization of a novel human member of the L1 gene family of neural cell adhesion molecules. Hum Genet,1998,103,355-64.
[42]Gu C H, Tong Q S, Zeng F Q, et al. Cloning and sequence analysis of TSEG-1, a novel gene specifically expressed in mouse testis. Yi Chuan,2008,30,352-8.
[43]Gu C, Tong Q, Zheng L, et al. TSEG-1, a novel member of histone H2A variants, participates in spematogenesis via promoting apoptosis of spermatogenic cells. Genomics,2010,95,278-89.
[1]de Kretser D M. Male infertility. Lancet,1997,349,787-90.
[2]Wieacker P, and Jakubiczka S. Genetic causes of male infertility. Andrologia,1997, 29,63-9.
[3]Ferlin A, Arredi B, and Foresta C. Genetic causes of male infertility. Reprod Toxicol,2006,22,133-41.
[4]Krausz C, and Giachini C. Genetic risk factors in male infertility. Arch Androl, 2007,53,125-33.
[5]Koji T, and Hishikawa Y. Germ cell apoptosis and its molecular trigger in mouse testes. Arch Histol Cytol,2003,66,1-16.
[6]Ferlin A, Raicu F, Gatta V, et al. Male infertility:role of genetic background. Reprod Biomed Online,2007,14,734-45.
[7]De Kretser D M, and Baker H W. Infertility in men:recent advances and continuing controversies. J Clin Endocrinol Metab,1999,84,3443-50.
[8]DeJong J. Basic mechanisms for the control of germ cell gene expression. Gene, 2006,366,39-50.
[9]O'Donnell L, Robertson K M, Jones M E, et al. Estrogen and spermatogenesis. Endocr Rev,2001,22,289-318.
[10]Lin Y N, and Matzuk M M. High-throughput discovery of germ-cell-specific genes. Semin Reprod Med,2005,23,201-12.
[11]Meade J D, Cho Y J, Shester B R, et al. Automated fluorescent differential display for cancer gene profiling. Methods Mol Biol,2010,576,99-133.
[12]Mizuno K, Kojima Y, Kurokawa S, et al. Identification of differentially expressed genes in human cryptorchid testes using suppression subtractive hybridization. J Urol,2009,181,1330-7; discussion 1337.
[13]Beissbarth T, Borisevich I, Horlein A, et al. Analysis of CREM-dependent gene expression during mouse spermatogenesis. Mol Cell Endocrinol,2003,212,29-39.
[14]Yu B. Role of in silico tools in gene discovery. Mol Biotechnol,2009,41,296-306.
[1] Sobhani K. Urine proteomic analysis: use of two-dimensional gel electrophoresis, isotope coded affinity tags, and capillary electrophoresis. Methods Mol Biol, 2010, 641,325-46.
[2] Choi H S, Lee S H, Kim H, et al. Germ cell-specific gene 1 targets testis-specific poly(A) polymerase to the endoplasmic reticulum through protein-protein interactions. FEBS Lett, 2008, 582,1203-9.
[3] Chen Y, and Xu D. Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae. Nucleic Acids Res, 2004, 32,6414-24.
[4] Golebiowski F, Tatham M H, Nakamura A, et al. High-stringency tandem affinity purification of proteins conjugated to ubiquitin-like moieties. Nat Protoc, 2010, 5,873-82.
[5] Paulucci-Holthauzen A A, Vergara L A, Bellot L J, et al. Spatial Distribution of Protein Kinase A Activity during Cell Migration Is Mediated by A-kinase Anchoring Protein AKAP Lbc. J Biol Chem, 2009, 284,5956-67.
[6] Chen T, Wang J, Xing D, et al. Spatio-temporal dynamic analysis of bid activation and apoptosis induced by alkaline condition in human lung adenocarcinoma cell. Cell Physiol Biochem, 2007, 20,569-78.
[7] Kang N, Duan L, Tang L, et al. Identification and characterization of a novel thymus aging related protein Rwddl. Cell Mol Immunol, 2008, 5,279-85.
[8] Babnigg G, and Joachimiak A. Predicting protein crystallization propensity from protein sequence. J Struct Funct Genomics, 2010, 11,71-80.
[9] Yoon S, and De Micheli G. Computational identification of microRNAs and their targets. Birth Defects Res C Embryo Today, 2006, 78,118-28.
[10] Ferlin A, Raicu F, Gatta V, et al. Male infertility: role of genetic background. Reprod Biomed Online, 2007, 14,734-45.
[11] Wang J, Ungar L H, Tseng H, et al. MetaProm: a neural network based meta-predictor for alternative human promoter prediction. BMC Genomics, 2007, 8,374.
[12] Chen C B, and Li T. A hybrid neural network system for prediction and recognition
of promoter regions in human genome. J Zhejiang Univ Sci B, 2005, 6,401-7.
[13] Keshavan R, Virata M, Keshavan A, et al. Computational identification of Ciona intestinalis microRNAs. Zoolog Sci, 2010, 27,162-70.
[14] Giampietri C, Petrungaro S, Coluccia P, et al. Germ cell apoptosis control during spermatogenesis. Contraception, 2005, 72,298-302.
[15] Ferlin A, Arredi B, and Foresta C. Genetic causes of male infertility. Reprod Toxicol, 2006, 22,133-41.
[16] Cooper H M, and Paterson Y. Production of polyclonal antisera. Curr Protoc Cytom, 2008, Appendix 3,Appendix 31.
[17] Cooper H M, and Paterson Y. Production of polyclonal antisera. Curr Protoc Neurosci, 2009, Chapter 5,Unit 5 5.
[1]Frericks M, and Esser C. A toolbox of novel murine house-keeping genes identified by meta-analysis of large scale gene expression profiles. Biochim Biophys Acta, 2008,1779.830-7.
[2]Wolkowicz M J, Coonrod S A. Reddi P P. et al. Refinement of the differentiated phenotype of the spermatogenic cell line GC-2 spd(ts). Biol Reprod,1996, 55,923-32.
[3]Hofmann M C. Narisawa S, Hess R A, et al. Immortalization of germ cells and somatic testicular cells using the SV40 large T antigen. Experimental Cell Research, 1992,201,417-35.
[4]Tsien R Y. The green fluorescent protein. Annu Rev Biochem,1998,67,509-44.
[5]March J C, Rao G, and Bentley W E. Biotechnological applications of green fluorescent protein. Appl Microbiol Biotechnol,2003,62,303-15.
[6]Yeh E, Gustafson K, and Boulianne G L. Green fluorescent protein as a vital marker and reporter of gene expression in Drosophila. Proc Natl Acad Sci U S A, 1995,92,7036-40.
[7]Chalfie M, Tu Y, Euskirchen G, et al. Green fluorescent protein as a marker for gene expression. Science,1994,263,802-5.
[8]Dan W B, Zhang C, Guan Z B, et al. Construction of bifunctional fusion proteins consisting of duck BAFF and EGFP. Biotechnol Lett,2008,30,221-7.
[9]Dai L C, Xu D Y, Yao X, et al. Construction of a fusion protein expression vector MK-EGFP and its subcellular localization in different carcinoma cell lines. World J Gastroenterol,2006,12,7649-53.
[10]Cao P, Zhang S, Zhang J, et al. Construction and characterization of a bi-functional EGFP/sBAFF fusion protein. Biochimie,2006,88,629-35.
[11]Zindy F, den Besten W, Chen B, et al. Control of spermatogenesis in mice by the cyclin D-dependent kinase inhibitors pl8(Ink4c) and pl9(Ink4d). Molecular and Cellular Biology,2001,21,3244-55.
[12]Lange C, Huttner W B, and Calegari F. Cdk4/cyclinD1 overexpression in neural stem cells shortens G1, delays neurogenesis, and promotes the generation and expansion of basal progenitors. Cell Stem Cell,2009,5,320-31.
[13]Zhou Z M, Sha J H, Li J M, et al. Expression of a novel reticulon-like gene in human testis. Reproduction,2002,123,227-34.
[14]Yazama F, Furuta K, Fujimoto M, et al. Abnormal spermatogenesis in mice unable to synthesize ascorbic acid. Anat Sci Int,2006,81,115-25.
[15]Namiki M, Koide T, Okuyama A, et al. Abnormality of testicular FSH receptors in infertile men. Acta Endocrinol (Copenh),1984,106,548-55.
[16]Diemer T, and Desjardins C. Developmental and genetic disorders in spermatogenesis. Hum Reprod Update,1999,5,120-40.
[17]Kim H H, and Schlegel P N. Endocrine manipulation in male infertility. Urol Clin North Am,2008,35,303-18, x.
[18]Scott L S. Fertility in cryptorchidism. Proc R Soc Med,1962,55,1047-50.
[19]Maclean J A,2nd, and Wilkinson M F. Gene regulation in spermatogenesis. Curr Top Dev Biol, 2005,71,131-97.
[20]Koji T, Izumi S, Tanno M, et al. Localization in situ of c-myc mRNA and c-myc protein in adult mouse testis. Histochem J,1988,20,551-7.
[21]Kotaja N, De Cesare D, Macho B, et al. Abnormal sperm in mice with targeted deletion of the act (activator of cAMP-responsive element modulator in testis) gene. Proc Natl Acad Sci U S A,2004,101,10620-5.
[22]Fan G C, Ren X, Qian J, et al. Novel cardioprotective role of a small heat-shock protein, Hsp20, against ischemia/reperfusion injury. Circulation,2005,111,1792-9.
[23]Fujisawa M, Shirakawa T, Fujioka H, et al. Adenovirus-mediated p53 gene transfer to rat testis impairs spermatogenesis. Arch Androl,2001,46,223-31.
[24]Vicini E, Loiarro M, Di Agostino S, et al.17-beta-estradiol elicits genomic and non-genomic responses in mouse male germ cells. J Cell Physiol,2006, 206,238-45.
[25]Beumer T L, Roepers-Gajadien H L, Gademan I S, et al. Apoptosis regulation in the testis:involvement of Bcl-2 family members. Mol Reprod Dev,2000,56,353-9.
[26]Grobholz R, Zentgraf H, Kohrmann K U, et al. Bax, Bcl-2, fas and Fas-L antigen expression in human seminoma:correlation with the apoptotic index. APMIS,2002, 110,724-32.
[27]Russell L D, Chiarini-Garcia H, Korsmeyer S J, et al. Bax-dependent spermatogonia apoptosis is required for testicular development and spermatogenesis. Biol Reprod,2002,66,950-8.
[28]Moreno R D, Lizama C, Urzua N, et al. Caspase activation throughout the first wave of spermatogenesis in the rat. Cell Tissue Res,2006,325,533-40.
[1]Pettersson A, Richiardi L, Nordenskjold A, et al. Age at surgery for undescended testis and risk of testicular cancer. N Engl J Med,2007,356,1835-41.
[2]Kaleva M, and Toppari J. Genetics and hormones in testicular descent. Hormones (Athens),2003,2,211-6.
[3]Rossato M, Tavolini I M, Calcagno A, et al. The novel hormone INSL3 is expressed in human testicular Leydig cell tumors:A clinical and immunohistochemical study. Urol Oncol,2008,
[4]Hafizur R M, Yano M, Gotoh T, et al. Modulation of chaperone activities of Hsp70 and Hsp70-2 by a mammalian DnaJ/Hsp40 homolog, DjA4. J Biochem,2004, 135,193-200.
[5]Krawczyk Z, Wisniewski J, and Biesiada E. A rat testis-specific hsp70 gene-related transcript is coded by a novel gene from the hsp70 multigene family. Acta Biochim Pol,1988,35,377-85.
[6]Sarge K D, and Cullen K E. Regulation of hsp expression during rodent spermatogenesis. Cell Mol Life Sci,1997,53,191-7.
[7]Hughes I A, and Acerini C L. Factors controlling testis descent. Eur J Endocrinol, 2008,159 Suppl 1,S75-82.
[8]Cartier R, Ren S V, Walther W, et al. In vivo gene transfer by low-volume jet injection. Anal Biochem,2000,282,262-5.
[9]Li E Z, Li D X, Zhang S Q, et al.17beta-estradiol stimulates proliferation of spermatogonia in experimental cryptorchid mice. Asian J Androl,2007,9,659-67.
[10]Xu J, Xu Z, Jiang Y, et al. Cryptorchidism induces mouse testicular germ cell apoptosis and changes in bcl-2 and bax protein expression. J Environ Pathol Toxicol Oncol,2000,19,25-33.
[11]Dalby B, Cates S, Harris A, et al. Advanced transfection with Lipofectamine 2000 reagent:primary neurons, siRNA, and high-throughput applications. Methods,2004, 33,95-103.
[12]Clements B A. Incani V, Kucharski C, et al. A comparative evaluation of poly-L-lysine-palmitic acid and Lipofectamine 2000 for plasmid delivery to bone marrow stromal cells. Biomaterials,2007,28,4693-704.
[13]Zhang Q, Cheng S X, Zhang X Z. et al. Water soluble polymer protected lipofectamine 2000/DNA complexes for solid-phase transfection. Macromol Biosci, 2009,9,1262-71.
[14]Bosco A P, Rhem R G, and Dolovich M B. In vitro estimations of in vivo jet nebulizer efficiency using actual and simulated tidal breathing patterns. J Aerosol Med,2005,18,427-38.
[15]Sawamura D, Ina S, Itai K, et al. In vivo gene introduction into keratinocytes using jet injection. Gene Ther,1999,6,1785-7.
[16]Walther W, Stein U, Fichtner I, et al. Nonviral in vivo gene delivery into tumors using a novel low volume jet-injection technology. Gene Ther,2001,8,173-80.
[17]Walther W, Stein U, Fichtner I, et al. Nonviral jet-injection gene transfer for efficient in vivo cytosine deaminase suicide gene therapy of colon carcinoma. Mol Ther,2005,12,1176-84.
[18]Walther W, Fichtner I, Schlag P M, et al. Nonviral jet-injection technology for intratumoral in vivo gene transfer of naked DNA. Methods Mol Biol,2009, 542,195-208.
[19]Giampietri C, Petrungaro S, Coluccia P, et al. Germ cell apoptosis control during spermatogenesis. Contraception,2005,72,298-302.
[20]Boekelheide K. Mechanisms of toxic damage to spermatogenesis. J Natl Cancer Inst Monogr,2005,6-8.
[21]Koji T, and Hishikawa Y. Germ cell apoptosis and its molecular trigger in mouse testes. Arch Histol Cytol,2003,66,1-16.
[22]Lecoeur H. Nuclear apoptosis detection by flow cytometry:influence of endogenous endonucleases. Exp Cell Res,2002,277,1-14.
[23]Mauduit C, Hamamah S, and Benahmed M. Stem cell factor/c-kit system in spermatogenesis. Hum Reprod Update,1999,5,535-45.
[24]Ferlin A, Raicu F, Gatta V, et al. Male infertility:role of genetic background. Reprod Biomed Online,2007,14,734-45.
[25]Rodriguez I, Ody C, Araki K, et al. An early and massive wave of germinal cell apoptosis is required for the development of functional spermatogenesis. EMBO J, 1997,16,2262-70.
[1]Moore JH. Bioinformatics. J Cell Physiol,2007,213:365-369.
[2]Wang JTL, Zaki MJ, Toivonen HTT, et al. Data Mining in Bioinformatics (Advanced information and knowledge processing).1st Edition. London, Springer-Verlag London Limited 2005.1-38.
[3]HU X, PAN Y. Knowledge Discovery In Bioinformatics:Techniques, Methods, and Applications. 1st Edition. Canada, Wiley interscience 2007.1-404.
[4]Hsu HH. Advanced Data Mining Technologies In Bioinformatics. Taiwan, Idea group publishing 2006:1-343.
[5]Lee JK, Williams PD, Cheon S. Data mining in genomics. Clin Lab Med,2008,28:145-166, viii.
[6]Zaki MJ, Karypis G, Yang J. Data Mining in Bioinformatics (BIOKDD). Algorithms Mol Biol, 2007,2:4.
[7]Bertone P, Gerstein M. Integrative data mining:the new direction in bioinformatics. IEEE Eng Med Biol Mag,2001,20:33-40.
[8]Cakmak A, Ozsoyoglu G. Mining biological networks for unknown pathways. Bioinformatics, 2007,23:2775-2783.
[9]Yang ZR, Hamer R. Bio-basis function neural networks in protein data mining. Curr Pharm Des, 2007,13:1403-1413.
[10]Prather JC, Lobach DF, Goodwin LK, et al. Medical data mining:knowledge discovery in a clinical data warehouse. Proc AMIA Annu Fall Symp,1997:101-105.
[11]Miyagawa T, Nishida N, Ohashi J, et al. Appropriate data cleaning methods for genome-wide association study. J Hum Genet,2008,53:886-893.
[12]Roberts BL, Anthony MK, Madigan EA, et al. Data management:cleaning and checking. Nurs Res, 1997,46:350-352.
[13]Herrero J, Diaz-Uriarte R, Dopazo J. Gene expression data preprocessing. Bioinformatics,2003, 19:655-656.
[14]Shinde K, Phatak M, Johannes FM, et al. Genomics Portals:integrative web-platform for mining genomics data. BMC Genomics,2010,11:27.
[15]Chen H, Ding L, Wu Z, et al. Semantic web for integrated network analysis in biomedicine. Brief Bioinform,2009,10:177-192.
[16]Baitaluk M, Ponomarenko J. Semantic Integration of Data on Transcriptional Regulation. Bioinformatics,2010.
[17]Lenz R, Beyer M, Kuhn KA. Semantic integration in healthcare networks. Int J Med Inform,2007, 76:201-207.
[18]Martin L, Anguita A, de la Calle G, et al. Semantic data integration in the European ACGT project. AMIA Annu Symp Proc,2007:1042.
[19]Regad L, Martin J, Nuel G, et al. Mining protein loops using a structural alphabet and statistical exceptionality. BMC Bioinformatics,2010,11:75.
[20]Zheng H, Chruszcz M, Lasota P, et al. Data mining of metal ion environments present in protein structures. J Inorg Biochem,2008,102:1765-1776.
[21]Exarchos TP, Papaloukas C, Lampros C, et al. Protein classification using sequential pattern mining. Conf Proc IEEE Eng Med Biol Soc,2006,1:5814-5817.
[22]Dobson PD, Cai YD, Stapley BJ, et al. Prediction of protein function in the absence of significant sequence similarity. Curr Med Chem,2004,11:2135-2142.
[23]Vinga S, Gouveia-Oliveira R, Almeida JS. Comparative evaluation of word composition distances for the recognition of SCOP relationships. Bioinformatics,2004,20:206-215.
[24]Hohl M, Ragan MA. Is multiple-sequence alignment required for accurate inference of phylogeny? Syst Biol,2007,56:206-221.
[25]Daraselia N, Yuryev A, Egorov S, et al. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks. BMC Bioinformatics,2007,8:243.
[26]Bhattacharya A, De RK. Bi-correlation clustering algorithm for determining a set of co-regulated genes. Bioinformatics,2009,25:2795-2801.
[27]Bales ME, Johnson SB. Graph theoretic modeling of large-scale semantic networks. J Biomed Inform,2006,39:451-464.
[28]Tseng GC. Quantile map:simultaneous visualization of patterns in many distributions with application to tandem mass spectrometry. Comput Stat Data Anal,2010,54:1124.
[29]Jupiter DC, VanBuren V. A visual data mining tool that facilitates reconstruction of transcription regulatory networks. PLoS One,2008,3:e1717.
[30]Curk T, Demsar J, Xu Q, et al. Microarray data mining with visual programming. Bioinformatics, 2005,21:396-398.
[31]Wegman EJ. Visual data mining. Stat Med,2003,22:1383-1397.
[32]Numao N, Kamimoto Y. Sequence Fourier analysis of a specific protein-DNA (RNA) interaction; an intermolecular frequency symmetry. Chem Pharm Bull (Tokyo).2009,57:1305-1307.
[33]Yin C, Yau SS. Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence. J Theor Biol,2007,247:687-694.
[34]Bulyk ML. Analysis of sequence specificities of DNA-binding proteins with protein binding microarrays. Methods Enzymol,2006,410:279-299.
[35]Rehm BH. Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification. Appl Microbiol Biotechnol,2001,57:579-592.
[36]Menashe I, Maeder D, Garcia-Closas M, et al. Pathway Analysis of Breast Cancer Genome-Wide Association Study Highlights Three Pathways and One Canonical Signaling Cascade. Cancer Res, 2010.
[37]Luo L, Peng G, Zhu Y, et al. Genome-wide gene and pathway analysis. Eur J Hum Genet,2010.
[38]Clemmensen A, Andersen KE, Clemmensen O, et al. Genome-Wide Expression Analysis of Human In Vivo Irritated Epidermis:Differential Profiles Induced by Sodium Lauryl Sulfate and Nonanoic Acid. J Invest Dermatol,2010.
[39]Kim S, Webster MJ. Integrative genome-wide association analysis of cytoarchitectural abnormalities in the prefrontal cortex of psychiatric disorders. Mol Psychiatry,2010.
[40]Lesch KP, Selch S, Renner TJ, et al. Genome-wide copy number variation analysis in attention-deficit/hyperactivity disorder:association with neuropeptide Y gene dosage in an extended pedigree. Mol Psychiatry,2010.
[41]Zhang L, Guo YF, Liu YZ, et al. Pathway-based genome-wide association analysis identified the importance of regulation-of-autophagy pathway for ultradistal radius BMD. J Bone Miner Res, 2010.
[42]Rollins B, Martin MV, Morgan L, et al. Analysis of whole genome biomarker expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet,2010.
[43]Bouzigon E. Forabosco P, Koppelman GH, et al. Meta-analysis of 20 genome-wide linkage studies evidenced new regions linked to asthma and atopy. Eur J Hum Genet,2010.
[44]Ivanov AS, Dubanov AV, Skvortsov VS, et al. [Computer drug design based on analysis of a target macromolecule structure. I. Search and description of a ligand binding site in a target molecule]. Vopr Med Khim,2002,48:304-315.
[45]Doherty RS, De Oliveira T, Seebregts C. et al. BioAfrica's HIV-1 proteomics resource:combining protein data with bioinformatics tools. Retrovirology,2005,2:18.
[46]Pattabiraman N. Analysis of ligand-macromolecule contacts:computational methods. Curr Med Chem,2002,9:609-621.
[47]Shen K, Tseng GC. Meta-analysis for pathway enrichment analysis when combining multiple genomic studies. Bioinformatics,2010,26:1316-1323.
[48]Arum CJ, Anderssen E, Tommeras K, et al. Gene expression profiling and pathway analysis of superficial bladder cancer in rats. Urology,2010,75:742-749.
[49]Zamar D, Tripp B, Ellis G, et al. Path:a tool to facilitate pathway-based genetic association analysis. Bioinformatics,2009,25:2444-2446.
[50]Elbers CC, van Eijk KR, Franke L, et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol,2009,33:419-431.
[51]Hu ZZ, Huang H, Cheema A, et al. Integrated Bioinformatics for Radiation-Induced Pathway Analysis from Proteomics and Microarray Data. J Proteomics Bioinform,2008,1:47-60.
[52]Zhou X, Wong ST. Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening. Proc IEEE Inst Electr Electron Eng,2008,96:1310-1331.
[53]Werner T. Bioinformatics applications for pathway analysis of microarray data. Curr Opin Biotechnol,2008,19:50-54.
[54][54] Zhang Y, Szustakowski J, Schinke M. Bioinformatics analysis of microarray data. Methods Mol Biol,2009,573:259-284.
[55]Rasnick D. DATE analysis:A general theory of biological change applied to microarray data. Biotechnol Prog,2009,25:1275-1288.
[56]Xia XQ, McClelland M, Porwollik S, et al. WebArrayDB:cross-platform microarray data analysis and public data repository. Bioinformatics,2009,25:2425-2429.
[57]Hauser PV, Perco P, Muhlberger I, et al. Microarray and bioinformatics analysis of gene expression in experimental membranous nephropathy. Nephron Exp Nephrol,2009,112:e43-58.
[58]Li C. Automating dChip:toward reproducible sharing of microarray data analysis. BMC Bioinformatics,2008,9:231.
[59]Moller-Levet CS, West CM, Miller CJ. Exploiting sample variability to enhance multivariate analysis of microarray data. Bioinformatics,2007,23:2733-2740.