EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences.

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns

From local laboratory data to public domain database in search of indirect association of diseases: AJAX based gene data search engine.

This paper presents an extensible schema for capturing laboratory gene variance data with its meta-data properties in a semi-structured environment. This paper also focuses on the issues of creating a local and task specific component database which is a subset of global data resources. An XML based genetic disorder component database schema is developed with adequate flexibilities to facilitate searching of gene mutation data. A web based search engine is developed that allows researchers to query a set of gene parameters obtained from local XML schema and subsequently allow them to automatically establish a link with the public domain gene databases. The application applies AJAX (Asynchronous Javascript and XML), a cutting-edge web technology, to carry out the gene data searching function

PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers

BACKGROUND: Long thought "relics" of evolution, not until recently have pseudogenes been of medical interest regarding regulation in cancer. Often, these regulatory roles are a direct by-product of their close sequence homology to protein-coding genes. Novel pseudogene-gene (PGG) functional associations can be identified through the integration of biomedical data, such as sequence homology, functional pathways, gene expression, pseudogene expression, and microRNA expression. However, not all of the information has been integrated, and almost all previous pseudogene studies relied on 1:1 pseudogene-parent gene relationships without leveraging other homologous genes/pseudogenes. RESULTS: We produce PGG families that expand beyond the current 1:1 paradigm. First, we construct expansive PGG databases by (i) CUDAlign graphics processing unit (GPU) accelerated local alignment of all pseudogenes to gene families (totaling 1.6 billion individual local alignments and >40,000 GPU hours) and (ii) BLAST-based assignment of pseudogenes to gene families. Second, we create an open-source web application (PseudoFuN [Pseudogene Functional Networks]) to search for integrative functional relationships of sequence homology, microRNA expression, gene expression, pseudogene expression, and gene ontology. We produce four "flavors" of CUDAlign-based databases (>462,000,000 PGG pairwise alignments and 133,770 PGG families) that can be queried and downloaded using PseudoFuN. These databases are consistent with previous 1:1 PGG annotation and also are much more powerful including millions of de novo PGG associations. For example, we find multiple known (e.g., miR-20a-PTEN-PTENP1) and novel (e.g., miR-375-SOX15-PPP4R1L) microRNA-gene-pseudogene associations in prostate cancer. PseudoFuN provides a "one stop shop" for identifying and visualizing thousands of potential regulatory relationships related to pseudogenes in The Cancer Genome Atlas cancers. CONCLUSIONS: Thousands of new PGG associations can be explored in the context of microRNA-gene-pseudogene co-expression and differential expression with a simple-to-use online tool by bioinformaticians and oncologists alike

A-Lister: a tool for analysis of differentially expressed omics entities across multiple pairwise comparisons.

BackgroundResearchers commonly analyze lists of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons. Large biological studies can involve multiple conditions, tissues, and timepoints that result in dozens of pairwise comparisons. Manually filtering and comparing lists of DEEs across multiple pairwise comparisons, typically done by writing custom code, is a cumbersome task that can be streamlined and standardized.ResultsA-Lister is a lightweight command line and graphical user interface tool written in Python. It can be executed in a differential expression mode or generic name list mode. In differential expression mode, A-Lister accepts as input delimited text files that are output by differential expression tools such as DESeq2, edgeR, Cuffdiff, and limma. To allow for the most flexibility in input ID types, to avoid database installation requirements, and to allow for secure offline use, A-Lister does not validate or impose restrictions on entity ID names. Users can specify thresholds to filter the input file(s) by column(s) such as p-value, q-value, and fold change. Additionally, users can filter the pairwise comparisons within the input files by fold change direction (sign). Queries composed of intersection, fuzzy intersection, difference, and union set operations can also be performed on any number of pairwise comparisons. Thus, the user can filter and compare any number of pairwise comparisons within a single A-Lister differential expression command. In generic name list mode, A-Lister accepts delimited text files containing lists of names as input. Queries composed of intersection, fuzzy intersection, difference, and union set operations can then be performed across these lists of names.ConclusionsA-Lister is a flexible tool that enables the user to rapidly narrow down large lists of DEEs to a small number of most significant entities. These entities can then be further analyzed using visualization, pathway analysis, and other bioinformatics tools

SymGRASS: a database of sugarcane orthologous genes involved in arbuscular mycorrhiza and root nodule symbiosis : from Seventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, (CIBB 2010), Palermo, Italy, 16 - 18 September 2010

Background: The rationale for gathering information from plants procuring nitrogen through symbiotic interactions controlled by a common genetic program for a sustainable biofuel production is the high energy demanding application of synthetic nitrogen fertilizers. We curated sequence information publicly available for the biofuel plant sugarcane, performed an analysis of the common SYM pathway known to control symbiosis in other plants, and provide results, sequences and literature links as an online database. Methods: Sugarcane sequences and informations were downloaded from the nucEST database, cleaned and trimmed with seqclean, assembled with TGICL plus translating mapping method, and annotated. The annotation is based on BLAST searches against a local formatted plant Uniprot90 generated with CD-HIT for functional assignment, rpsBLAST to CDD database for conserved domain analysis, and BLAST search to sorghum's for Gene Ontology (GO) assignment. Gene expression was normalized according the Unigene standard, presented as ESTs/100 kb. Protein sequences known in the SYM pathway were used as queries to search the SymGRASS sequence database. Additionally, antimicrobial peptides described in the PhytAMP database served as queries to retrieve and generate expression profiles of these defense genes in the libraries compared to the libraries obtained under symbiotic interactions. Results: We describe the SymGRASS, a database of sugarcane orthologous genes involved in arbuscular mycorrhiza (AM) and root nodule (RN) symbiosis. The database aggregates knowledge about sequences, tissues, organ, developmental stages and experimental conditions, and provides annotation and level of gene expression for sugarcane transcripts and SYM orthologous genes in sugarcane through a web interface. Several candidate genes were found for all nodes in the pathway, and interestingly a set of symbiosis specific genes was found. Conclusions: The knowledge integrated in SymGRASS may guide studies on molecular, cellular and physiological mechanisms by which sugarcane controls the establishment and efficiency of endophytic associations. We believe that the candidate sequences for the SYM pathway together with the pool of exclusively expressed tentative consensus (TC) sequences are crucial for the design of molecular studies to unravel the mechanisms controlling the establishment of symbioses in sugarcane, ultimately serving as a basis for the improvement of grass crops

The low recombining pericentromeric region of barley restricts gene diversity and evolution but not gene expression

The low-recombining pericentromeric region of the barley genome contains roughly a quarter of the genes of the species, embedded in low-recombining DNA that is rich in repeats and repressive chromatin signatures. We have investigated the effects of pericentromeric region residency upon the expression, diversity and evolution of these genes. We observe no significant difference in average transcript level or developmental RNA specificity between the barley pericentromeric region and the rest of the genome. In contrast, all of the evolutionary parameters studied here show evidence of compromised gene evolution in this region. First, genes within the pericentromeric region of wild barley show reduced diversity and significantly weakened purifying selection compared with the rest of the genome. Second, gene duplicates (ohnolog pairs) derived from the cereal whole-genome duplication event ca. 60MYa have been completely eliminated from the barley pericentromeric region. Third, local gene duplication in the pericentromeric region is reduced by 29% relative to the rest of the genome. Thus, the pericentromeric region of barley is a permissive environment for gene expression but has restricted gene evolution in a sizeable fraction of barley's genes