Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Cloud computing has become the most popular computing architecture in the world.The multiplicity of available platforms for end users makes it very inconvenient for service providers to manage, calling for a unified approach for resource management and security in virtualized data centers in order to achieve seamless cloud management.In this paper, we present data center management as a service (DMaaS) as such a unified approach, using multi-tenant software.Virtualized data centers pool cloud resources used to meet enterprise needs, mostly related to storage and processing.The proposed work provides a service model for the cloud infrastructure and supports multiple tenants to connect their data center management in a shared and secure manner.Our service manages disparate virtual environments using a single interface.

SUMMARY: Statistical validation of peptide assignments from a large-scale shotgun proteomics experiment is a critical step, and various methods for evaluating significance based on decoy database search are in practice. False discovery rate (FDR) estimation of peptide assignments assesses global significance and corrects for multiple comparisons. Various approaches have been proposed for FDR estimation but unavailability of standard tools or libraries leads to development of many in-house scripts followed by manual steps that are error-prone and low-throughput. The ProteoStats library provides an open-source framework for developers with many FDR estimation and visualization features for several popular search algorithms. It also provides accurate q-values, which can be easily integrated in any proteomics pipeline to provide automated, accurate, high-throughput statistical validation and minimize manual errors. AVAILABILITY: https://sourceforge.net/projects/mssuite/files/ProteoStats/. CONTACT: [email protected] or [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Absence of any regular structure is increasingly being observed in structural studies of proteins. These disordered regions or random coils, which have been observed under physiological conditions, are indicators of protein plasticity. The wide variety of interactions possible due to the flexibility of these 'natively disordered' regions confers functional advantage to the protein and the organism in general. This concept is underscored by the increasing proportion of intrinsically unstructured proteins seen with the ascension in the complexity of the organisms. The 'natively unfolded/disordered' state of the protein can be predicted utilizing Uversky's or Dunker's algorithm. We utilized Uversky's prediction scheme and based on the unique position of a protein in the charge-hydrophobicity plot, a derived net score was used to predict the overall disorder of the human housekeeping and non-housekeeping proteins. Substantial numbers of proteins in both the classes were predicted to be unfolded. However, comparative genomic analysis of predicted unfolded Homo sapiens proteins with homologues in Caenorhabditis elegans, Drosophila melanogaster and Mus musculus revealed significant increase in unfoldedness in non-housekeeping proteins in comparison with housekeeping proteins. Our analysis in the evolutionary context suggests addition or substitution of amino acid residues which favour unfoldedness in non-housekeeping proteins compared to housekeeping proteins.

UNLABELLED: We present the development of a Comprehensive database of 12 076 invariant Peptide Signatures (CoPS) derived from 52 bacterial genomes with a minimum occurrence in at least seven organisms. These peptides were observed in functionally similar proteins and are distributed over nearly 1250 different functional proteins. The database provides function, structure and occurrence in biochemical pathways of the proteins containing these signature peptides. It houses additional information on the signature peptides, such as identical match in other motif/pattern (e.g. PROSITE, BLOCKS, PRINTS and Pfam) databases and the database of interacting proteins, human proteome and mutation effect on these signature peptides. There is a wide applicability of this database in the identification of critical functional residues in proteins. The database also facilitates the identification of folding nucleus/structural determinants in proteins and functional assignment to yet unknown proteins. We demonstrate functional assignment to 2605 hypothetical proteins in bacterial genomes and 112 unknown proteins in human using this database. AVAILABILITY: The database can be freely accessed through the following URL: http://203.195.151.46/copsv2/index.html or http://203.90.127.70/copsv2/index.html

MOTIVATION: The recent outbreak of severe acute respiratory syndrome (SARS) caused by SARS coronavirus (SARS-CoV) has necessitated an in-depth molecular understanding of the virus to identify new drug targets. The availability of complete genome sequence of several strains of SARS virus provides the possibility of identification of protein-coding genes and defining their functions. Computational approach to identify protein-coding genes and their putative functions will help in designing experimental protocols. RESULTS: In this paper, a novel analysis of SARS genome using gene prediction method GeneDecipher developed in our laboratory has been presented. Each of the 18 newly sequenced SARS-CoV genomes has been analyzed using GeneDecipher. In addition to polyprotein 1ab(1), polyprotein 1a and the four genes coding for major structural proteins spike (S), small envelope (E), membrane (M) and nucleocapsid (N), six to eight additional proteins have been predicted depending upon the strain analyzed. Their lengths range between 61 and 274 amino acids. Our method also suggests that polyprotein 1ab, polyprotein 1a, S, M and N are proteins of viral origin and others are of prokaryotic. Putative functions of all predicted protein-coding genes have been suggested using conserved peptides present in their open reading frames. AVAILABILITY: Detailed results of GeneDecipher analysis of all the 18 strains of SARS-CoV genomes are available at http://www.igib.res.in/sarsanalysis.html