Biomedical subjects

Nicholas Carriero

Publications and source records attributed to Nicholas Carriero.

5 recordsLinked to original sources

PubMed2006

Pseudogene.org: a comprehensive database and comparison platform for pseudogene annotation.

The Pseudogene.org knowledgebase serves as a comprehensive repository for pseudogene annotation. The definition of a pseudogene varies within the literature, resulting in significantly different approaches to the problem of identification. Consequently, it is difficult to maintain a consistent collection of pseudogenes in detail necessary for their effective use. Our database is designed to address this issue. It integrates a variety of heterogeneous resources and supports a subset structure that highlights specific groups of pseudogenes that are of interest to the research community. Tools are provided for the comparison of sets and the creation of layered set unions, enabling researchers to derive a current 'consensus' set of pseudogenes. Additional features include versatile search, the capacity for robust interaction with other databases, the ability to reconstruct older versions of the database (accounting for changing genome builds) and an underlying object-oriented interface designed for researchers with a minimal knowledge of programming. At the present time, the database contains more than 100,000 pseudogenes spanning 64 prokaryote and 11 eukaryote genomes, including a collection of human annotations compiled from 16 sources.

Databases, Genetic↗

PubMed2006

PseudoPipe: an automated pseudogene identification pipeline.

MOTIVATION: Mammalian genomes contain many 'genomic fossils' i.e. pseudogenes. These are disabled copies of functional genes that have been retained in the genome by gene duplication or retrotransposition events. Pseudogenes are important resources in understanding the evolutionary history of genes and genomes. RESULTS: We have developed a homology-based computational pipeline ('PseudoPipe') that can search a mammalian genome and identify pseudogene sequences in a comprehensive and consistent manner. The key steps in the pipeline involve using BLAST to rapidly cross-reference potential "parent" proteins against the intergenic regions of the genome and then processing the resulting "raw hits" -- i.e. eliminating redundant ones, clustering together neighbors, and associating and aligning clusters with a unique parent. Finally, pseudogenes are classified based on a combination of criteria including homology, intron-exon structure, and existence of stop codons and frameshifts.

Algorithms↗

PubMed2005

Transcribed processed pseudogenes in the human genome: an intermediate form of expressed retrosequence lacking protein-coding ability.

Pseudogenes, in the case of protein-coding genes, are gene copies that have lost the ability to code for a protein; they are typically identified through annotation of disabled, decayed or incomplete protein-coding sequences. Processed pseudogenes (PPsigs) are made through mRNA retrotransposition. There is overwhelming genomic evidence for thousands of human PPsigs and also dozens of human processed genes that comprise complete retrotransposed copies of other genes. Here, we survey for an intermediate entity, the transcribed processed pseudogene (TPPsig), which is disabled but nonetheless transcribed. TPPsigs may affect expression of paralogous genes, as observed in the case of the mouse makorin1-p1 TPPsig. To elucidate their role, we identified human TPPsigs by mapping expressed sequences onto PPsigs and, reciprocally, extracting TPPsigs from known mRNAs. We consider only those PPsigs that are homologous to either non-mammalian eukaryotic proteins or protein domains of known structure, and require detection of identical coding-sequence disablements in both the expressed and genomic sequences. Oligonucleotide microarray data provide further expression verification. Overall, we find 166-233 TPPsigs ( approximately 4-6% of PPsigs). Proteins/transcripts with the highest numbers of homologous TPPsigs generally have many homologous PPsigs and are abundantly expressed. TPPsigs are significantly over-represented near both the 5' and 3' ends of genes; this suggests that TPPsigs can be formed through gene-promoter co-option, or intrusion into untranslated regions. However, roughly half of the TPPsigs are located away from genes in the intergenic DNA and thus may be co-opting cryptic promoters of undesignated origin. Furthermore, TPPsigs are unlike other PPsigs and processed genes in the following ways: (i) they do not show a significant tendency to either deposit on or originate from the X chromosome; (ii) only 5% of human TPPsigs have potential orthologs in mouse. This latter finding indicates that the vast majority of TPPsigs is lineage specific. This is likely linked to well-documented extensive lineage-specific SINE/LINE activity. The list of TPPsigs is available at: http://www.biology.mcgill.ca/faculty/harrison/tppg/bppg.tov (or) http:pseudogene.org.

Amino Acid Sequence↗

PubMed2004

A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Amino Acid Sequence↗

PubMed2003

A "polyORFomic" analysis of prokaryote genomes using disabled-homology filtering reveals conserved but undiscovered short ORFs.

Prokaryote gene annotation is complicated by large numbers of short open reading frames (ORFs) that arise naturally from genetic code design. Historically, many hypothetical ORFs have been annotated as genes in microbes, usually with an arbitrary length threshold (e.g. greater than 100 codons). Given the use of such thresholds, what is the extent of genuine undiscovered short genes in the current sampling of prokaryote genomes? To assess rigorously the potential under-annotation of short ORFs with homology, we exhaustively compared the polyORFome--all possible ORFs in 64 prokaryotes (53 bacteria and 11 archaea) plus budding yeast--to itself and to all known proteins. The novelty of our analysis is that, firstly, sequence comparisons to/between both annotated and un-annotated ORFs are considered, and secondly a two-step disabled-homology filter is applied to set aside putative pseudogenes and spurious ORFs. We find that un-annotated homologous short ORFs (uhORFs) correspond to a small but non-negligible fraction of the annotated prokaryote proteomes (0.5-3.8%, depending on selection criteria). Moreover, the disabled-homology filter indicates that about a third of uhORFs correspond to putative pseudogenes or spurious ORFs. Our analysis shows that the use of annotation length thresholds is unnecessary, as there are manageable numbers of short ORF homologies conserved (without disablements) across microbial genomes. Data on uhORFs are available from http://pseudogene.org/polyo

Conserved Sequence↗