Genomics, Proteomics & Bioinformatics (GPB) Has a New Start—Open Access
We are now presenting to our readers the first issue of
Volume 10 of Genomics, Proteomics & Bioinformatics
(GPB). It is also the first issue for the new status.
Distinct Contributions of Replication and Transcription to Mutation Rate Variation of Human Genomes
Peng Cui, Feng Ding, Qiang Lin, Lingfang Zhang, Ang Li, Zhang Zhang, Songnian Hu,Jun Yua
Here, we evaluate the contribution of two major biological processes—DNA replication and transcription—to mutation rate variation in human genomes. Based on analysis of the public human tissue transcriptomics data, high-resolution replicating map of Hela cells and dbSNP data, we present significant correlations between expression breadth, replication time in local regions and SNP density. SNP density of tissue-specific (TS) genes is significantly higher than that of housekeeping (HK) genes. TS genes tend to locate in late-replicating genomic regions and genes in such regions have a higher SNP density compared to those in early-replication regions. In addition, SNP density is found to be positively correlated with expression level among HK genes. We conclude that the process of DNA replication generates stronger mutational pressure than transcription-associated biological processes do, resulting in an increase of mutation rate in TS genes while having weaker effects on HK genes. In contrast, transcription-associated processes are mainly responsible for the accumulation of mutations in highly-expressed HK genes.
The Transcript-centric Mutations in Human Genomes
Peng Cui, Qiang Lin, Feng Ding, Songnian Hu, Jun Yu
Since the human genome is mostly transcribed, genetic variations must exhibit sequence signatures reflecting the relationship between transcription processes and chromosomal structures as we have observed in unicellular organisms. In this study, a set of 646 ubiquitous expression-invariable genes (EIGs) which are present in germline cells were defined and examined based on RNA-sequencing data from multiple high-throughput transcriptomic data. We demonstrated a relationship between gene expression level and transcript-centric mutations in the human genome based on single nucleotide polymorphism (SNP) data. A significant positive correlation was shown between gene expression and mutation, where highly-expressed genes accumulate more mutations than lowly-expressed genes. Furthermore, we found four major types of transcript-centric mutations: C→T, A→G, C→G, and G→T in human genomes and identified a negative gradient of the sequence variations aligning from the 5′ end to the 3′ end of the transcription units (TUs). The periodical occurrence of these genetic variations across TUs is associated with nucleosome phasing. We propose that transcript-centric mutations are one of the major driving forces for gene and genome evolution along with creation of new genes, gene/genome duplication, and horizontal gene transfer.
Mining Functional Gene Modules Linked with Rheumatoid Arthritis Using a SNP-SNP Network
Lin Hua, Hui Lin, Dongguo Li, Lin Li, Zhicheng Liu
The identification of functional gene modules that are derived from integration of information from different types of networks is a powerful strategy for interpreting the etiology of complex diseases such as rheumatoid arthritis (RA). Genetic variants are known to increase the risk of developing RA. Here, a novel method, the construction of a genetic network, was used to mine functional gene modules linked with RA. A polymorphism interaction analysis (PIA) algorithm was used to obtain cooperating single nucleotide polymorphisms (SNPs) that contribute to RA disease. The acquired SNP pairs were used to construct a SNP-SNP network. Sub-networks defined by hub SNPs were then extracted and turned into gene modules by mapping SNPs to genes using dbSNP database. We performed Gene Ontology (GO) analysis on each gene module, and some GO terms enriched in the gene modules can be used to investigate clustered gene function for better understanding RA pathogenesis. This method was applied to the Genetic Analysis Workshop 15 (GAW 15) RA dataset. The results show that genes involved in functional gene modules, such as CD160 (rs744877) and RUNX1 (rs2051179), are especially relevant to RA, which is supported by previous reports. Furthermore, the 43 SNPs involved in the identified gene modules were found to be the best classifiers when used as variables for sample classification.
MotViz: A Tool for Sequence Motif Prediction in Parallel to Structural Visualization and Analyses
Muhammad Sulaman Nawaz, Sajid Rashid
Linking similar proteins structurally is a challenging task that may help in finding the novel members of a protein family. In this respect, identification of conserved sequence can facilitate understanding and classifying the exact role of proteins. However, the exact role of these conserved elements cannot be elucidated without structural and physiochemical information. In this work, we present a novel desktop application MotViz designed for searching and analyzing the conserved sequence segments within protein structure. With MotViz, the user can extract a complete list of sequence motifs from loaded 3D structures, annotate the motifs structurally and analyze their physiochemical properties. The conservation value calculated for an individual motif can be visualized graphically. To check the efficiency, predicted motifs from the data sets of 9 protein families were analyzed and MotViz algorithm was more efficient in comparison to other online motif prediction tools. Furthermore, a database was also integrated for storing, retrieving and performing the detailed functional annotation studies. In summary, MotViz effectively predicts motifs with high sensitivity and simultaneously visualizes them into 3D strucures. Moreover, MotViz is user-friendly with optimized graphical parameters and better processing speed due to the inclusion of a database at the back end. MotViz is available at http://www.fi-pk.com/motviz.html.
SMS 2.0: An Updated Database to Study the Structural Plasticity of Short Peptide Fragments in Non-redundant Proteins
Dheeraj Ravella, Muthukumarasamy Uthaya Kumar, Durairaj Sherlin, Mani Shankar, Marthandan Kirti Vaishnavi, Kanagaraj Sekar
The function of a protein molecule is greatly influenced by its three-dimensional (3D) structure and therefore structure prediction will help identify its biological function. We have updated Sequence, Motif and Structure (SMS), the database of structurally rigid peptide fragments, by combining amino acid sequences and the corresponding 3D atomic coordinates of non-redundant (25%) and redundant (90%) protein chains available in the Protein Data Bank (PDB). SMS 2.0 provides information pertaining to the peptide fragments of length 5–14 residues. The entire dataset is divided into three categories, namely, same sequence motifs having similar, intermediate or dissimilar 3D structures. Further, options are provided to facilitate structural superposition using the program structural alignment of multiple proteins (STAMP) and the popular JAVA plug-in (Jmol) is deployed for visualization. In addition, functionalities are provided to search for the occurrences of the sequence motifs in other structural and sequence databases like PDB, Genome Database (GDB), Protein Information Resource (PIR) and Swiss-Prot. The updated database along with the search engine is available over the World Wide Web through the following URL http://cluster.physics.iisc.ernet.in/sms/.
Gene2DGE: A Perl Package for Gene Model Renewal with Digital Gene Expression Data
Xiaoli Tanga, #, Libin Deng, Dake Zhang, Jiari Lin, Yi Wei, Qinqin Zhou, Xiang Li, Guilin Li, Shangdong Liang
For transcriptome analysis, it is critical to precisely define all the transcripts across the whole genome. More and more digital gene expression (DGE) scannings have indicated the presence of huge amount of novel transcripts in addition to the known gene models. However, almost all these studies still depend crucially on existing annotation. Here, we present Gene2DGE, a Perl software package for gene model renewal with DGE data. We applied Gene2DGE to the mouse blastomere transcriptome, and defined 98,532 read-enriched regions (RERs) by read clustering supported by more than four reads for each base pair. Taking advantage of this ab initio method, we refined 2,104 exonic regions (4% of a total of 48,501 annotated transcribed regions) with remarkable extension into un-annotated regions (>50 bp). For 5% of uniquely mapped reads falling within intron regions, we identified 13,291 additional possible exons. As a result, we renewed 4,788 gene models, which account for 39% of a total of 12,277 transcribed genes. Furthermore, we identified 12,613 intergenic RERs, suggesting the possible presence of novel genes outside the existing gene models. In this study, therefore, we have developed a suitable tool for renewal of known gene models by ab initio prediction in transcriptome dissection. The Gene2DGE package is freely available at http://bighapmap.big.ac.cn/.