Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Tbl2KnownGene: A command-line program to convert NCBI.tbl to UCSC knownGene.txt data file.

ABSTRACT:

Unlabelled

The schema for UCSC Known Genes (knownGene.txt) has been widely adopted for use in both standard and custom downstream analysis tools/scripts. For many popular model organisms (e.g. Arabidopsis), sequence and annotation data tables (including "knownGene.txt") have not yet been made available to the public. Therefore, it is of interest to describe Tbl2KnownGene, a .tbl file parser that can process the contents of a NCBI .tbl file and produce a UCSC Known Genes annotation feature table. The algorithm is tested with chromosome datasets from Arabidopsis genome (TAIR10). The Tbl2KnownGene parser finds utility for data with other organisms having similar .tbl annotations.

Availability

Perl scripts and required input files are available on the web at http://thoth.indstate.edu/~ybai2/Tbl2KnownGene/ index.html.

SUBMITTER: Bai Y

PROVIDER: S-EPMC4166776 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Tbl2KnownGene: A command-line program to convert NCBI.tbl to UCSC knownGene.txt data file.

Bai Yongsheng Y

Bioinformation 20140830 8

<h4>Unlabelled</h4>The schema for UCSC Known Genes (knownGene.txt) has been widely adopted for use in both standard and custom downstream analysis tools/scripts. For many popular model organisms (e.g. Arabidopsis), sequence and annotation data tables (including "knownGene.txt") have not yet been made available to the public. Therefore, it is of interest to describe Tbl2KnownGene, a .tbl file parser that can process the contents of a NCBI .tbl file and produce a UCSC Known Genes annotation featur ...[more]

PMID: 25258492

Similar Datasets

PyBDA: a command line tool for automated analysis of big biological data sets.

Project description:BACKGROUND:Analysing large and high-dimensional biological data sets poses significant computational difficulties for bioinformaticians due to lack of accessible tools that scale to hundreds of millions of data points. RESULTS:We developed a novel machine learning command line tool called PyBDA for automated, distributed analysis of big biological data sets. By using Apache Spark in the backend, PyBDA scales to data sets beyond the size of current applications. It uses Snakemake in order to automatically schedule jobs to a high-performance computing cluster. We demonstrate the utility of the software by analyzing image-based RNA interference data of 150 million single cells. CONCLUSION:PyBDA allows automated, easy-to-use data analysis using common statistical methods and machine learning algorithms. It can be used with simple command line calls entirely making it accessible to a broad user base. PyBDA is available at https://pybda.rtfd.io.

| S-EPMC6849186 | biostudies-literature

From command-line bioinformatics to bioGUI.

Project description:Bioinformatics is a highly interdisciplinary field providing (bioinformatics) applications for scientists from many disciplines. Installing and starting applications on the command-line (CL) is inconvenient and/or inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step toward routinely making CL-only applications available to more scientists and, thus, toward a more effective interdisciplinary work. With our bioGUI framework we address two main problems of using CL bioinformatics applications: First, many tools work on UNIX-systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using CL tools which, however, could well support them in their research. With bioGUI install modules and templates, installing and using CL tools is made possible for most scientists-even on Windows, due to bioGUI's support for Windows Subsystem for Linux. In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install and use bioinformatics tools with just a few clicks.

| S-EPMC6875409 | biostudies-literature

RVD: a command-line program for ultrasensitive rare single nucleotide variant detection using targeted next-generation DNA resequencing.

Project description:Rare single nucleotide variants play an important role in genetic diversity and heterogeneity of specific human disease. For example, an individual clinical sample can harbor rare mutations at minor frequencies. Genetic diversity within an individual clinical sample is oftentimes reflected in rare mutations. Therefore, detecting rare variants prior to treatment may prove to be a useful predictor for therapeutic response. Current rare variant detection algorithms using next generation DNA sequencing are limited by inherent sequencing error rate and platform availability.Here we describe an optimized implementation of a rare variant detection algorithm called RVD for use in targeted gene resequencing. RVD is available both as a command-line program and for use in MATLAB and estimates context-specific error using a beta-binomial model to call variants with minor allele frequency (MAF) as low as 0.1%. We show that RVD accepts standard BAM formatted sequence files. We tested RVD analysis on multiple Illumina sequencing platforms, among the most widely used DNA sequencing platforms.RVD meets a growing need for highly sensitive and specific tools for variant detection. To demonstrate the usefulness of RVD, we carried out a thorough analysis of the software's performance on synthetic and clinical virus samples sequenced on both an Illumina GAIIx and a MiSeq. We expect RVD can improve understanding the genetics and treatment of common viral diseases including influenza. RVD is available at the following URL:http://dna-discovery.stanford.edu/software/rvd/.

| S-EPMC3695852 | biostudies-other

UCSC Data Integrator and Variant Annotation Integrator.

Project description:UnlabelledTwo new tools on the UCSC Genome Browser web site provide improved ways of combining information from multiple datasets, optionally including the user's own custom track data and/or data from track hubs. The Data Integrator combines columns from multiple data tracks, showing all items from the first track along with overlapping items from the other tracks. The Variant Annotation Integrator is tailored to adding functional annotations to variant calls; it offers a more restricted set of underlying data tracks but adds predictions of each variant's consequences for any overlapping or nearby gene transcript. When available, it optionally adds additional annotations including effect prediction scores from dbNSFP for missense mutations, ENCODE regulatory summary tracks and conservation scores.Availability and implementationThe web tools are freely available at http://genome.ucsc.edu/ and the underlying database is available for download at http://hgdownload.cse.ucsc.edu/ The software (written in C and Javascript) is available from https://genome-store.ucsc.edu/ and is freely available for academic and non-profit usage; commercial users must obtain a license.Contactangie@soe.ucsc.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4848401 | biostudies-literature

ViralRecall-A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in 'Omic Data.

Project description:Giant viruses are widespread in the biosphere and play important roles in biogeochemical cycling and host genome evolution. Also known as nucleo-cytoplasmic large DNA viruses (NCLDVs), these eukaryotic viruses harbor the largest and most complex viral genomes known. Studies have shown that NCLDVs are frequently abundant in metagenomic datasets, and that sequences derived from these viruses can also be found endogenized in diverse eukaryotic genomes. The accurate detection of sequences derived from NCLDVs is therefore of great importance, but this task is challenging owing to both the high level of sequence divergence between NCLDV families and the extraordinarily high diversity of genes encoded in their genomes, including some encoding for metabolic or translation-related functions that are typically found only in cellular lineages. Here, we present ViralRecall, a bioinformatic tool for the identification of NCLDV signatures in 'omic data. This tool leverages a library of giant virus orthologous groups (GVOGs) to identify sequences that bear signatures of NCLDVs. We demonstrate that this tool can effectively identify NCLDV sequences with high sensitivity and specificity. Moreover, we show that it can be useful both for removing contaminating sequences in metagenome-assembled viral genomes as well as the identification of eukaryotic genomic loci that derived from NCLDV. ViralRecall is written in Python 3.5 and is freely available on GitHub: https://github.com/faylward/viralrecall.

| S-EPMC7909515 | biostudies-literature

Hybkit: a Python API and command-line toolkit for hybrid sequence data from chimeric RNA methods.

Project description:SummaryExperimental methods using microRNA/target ligation have recently provided significant insights into microRNA functioning through generation of chimeric (hybrid) RNA sequences. Here, we introduce Hybkit, a Python3 API, and command-line toolkit for analysis of hybrid sequence data in the "hyb" file format to enable customizable evaluation and annotation of hybrid characteristics. The Hybkit API includes a suite of python objects for developing custom analyses of hybrid data as well as miRNA-specific analysis methods, built-in plotting of analysis results, and incorporation of predicted miRNA/target interactions in Vienna format.Availability and implementationHybkit is provided free and open source under the GNU GPL license at github.com/RenneLab/hybkit and archived on Zenodo (doi.org/10.5281/zenodo.7834299). Hybkit distributions are also provided via PyPI (pypi.org/project/hybkit), Conda (bioconda.github.io/recipes/hybkit/README.html), and Docker (quay.io/repository/biocontainers/hybkit).

| S-EPMC10701094 | biostudies-literature

Plann: A command-line application for annotating plastome sequences.

Project description:PREMISE OF THE STUDY:Plann automates the process of annotating a plastome sequence in GenBank format for either downstream processing or for GenBank submission by annotating a new plastome based on a similar, well-annotated plastome. METHODS AND RESULTS:Plann is a Perl script to be executed on the command line. Plann compares a new plastome sequence to the features annotated in a reference plastome and then shifts the intervals of any matching features to the locations in the new plastome. Plann's output can be used in the National Center for Biotechnology Information's tbl2asn to create a Sequin file for GenBank submission. CONCLUSIONS:Unlike Web-based annotation packages, Plann is a locally executable script that will accurately annotate a plastome sequence to a locally specified reference plastome. Because it executes from the command line, it is ready to use in other software pipelines and can be easily rerun as a draft plastome is improved.

| S-EPMC4542940 | biostudies-literature

ASCIIGenome: a command line genome browser for console terminals.

Project description:SummaryCurrent genome browsers are designed to work via graphical user interfaces (GUIs), which, however intuitive, are not amenable to operate within console terminals and therefore are difficult to streamline or integrate in scripts. To circumvent these limitations, ASCIIGenome runs exclusively via command line interface to display genomic data directly in a terminal window. By following the same philosophy of UNIX tools, ASCIIGenome aims to be easily integrated with the command line, including batch processing of data, and therefore enables an effective exploration of the data.ImplementationASCIIGenome is written in Java. Consequently, it is a cross-platform tool and requires minimal or no installation. Some of the common genomic data types are supported and data access on remote ftp servers is possible. Speed and memory footprint are comparable to or better than those of common genome browsers.Availability and implementationSoftware and source code (MIT License) are available at https://github.com/dariober/ASCIIGenome with detailed documentation at http://asciigenome.readthedocs.io .ContactDario.beraldi@cruk.cam.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5423454 | biostudies-literature

Facile: a command-line network compiler for systems biology.

Project description:BackgroundA goal of systems biology is the quantitative modelling of biochemical networks. Yet for many biochemical systems, parameter values and even the existence of interactions between some chemical species are unknown. It is therefore important to be able to easily investigate the effects of adding or removing reactions and to easily perform a bifurcation analysis, which shows the qualitative dynamics of a model for a range of parameter values.ResultsWe present Facile, a Perl command-line tool for analysing the dynamics of a systems biology model. Facile implements the law of mass action to automatically compile a biochemical network (written as, for example, E + S <-> C) into scripts for analytical analysis (Mathematica and Maple), for simulation (XPP and Matlab), and for bifurcation analysis (AUTO). Facile automatically identifies mass conservations and generates the reduced form of a model with the minimum number of independent variables. This form is essential for bifurcation analysis, and Facile produces a C version of the reduced model for AUTO.ConclusionFacile is a simple, yet powerful, tool that greatly accelerates analysis of the dynamics of a biochemical network. By acting at the command-line and because of its intuitive, text-based input, Facile is quick to learn and can be incorporated into larger programs or into automated tasks.

| S-EPMC1976619 | biostudies-literature

UCSC Cell Browser: Visualize Your Single-Cell Data.

Project description:SummaryAs the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets.Availability and implementationWe provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC8652023 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data