verifyBamID 1.1.3 – Identify Contamination of Sample Swap in Sequence data

verifyBamID 1.1.3

:: DESCRIPTION

verifyBamID is a software that verifies whether the reads in particular file match previously known genotypes for an individual (or group of individuals), and checks whether the reads are contaminated as a mixture of two samples

::DEVELOPER

Abecasis Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
:: DOWNLOAD

 verifyBamID

:: MORE INFORMATION

Citation

G. Jun, M. Flickinger, K. N. Hetrick, Kurt, J. M. Romm, K. F. Doheny, G. Abecasis, M. Boehnke,and H. M. Kang,
Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data,
Am J Hum Genet. 2012 Nov 2;91(5):839-48. doi: 10.1016/j.ajhg.2012.09.004. (volume 91 issue 5 pp.839 – 848)

MISO 0.5.4 – RNA-Seq Experiments for Identifying Isoform Regulation

MISO 0.5.4

:: DESCRIPTION

MISO (Mixture of Isoforms) is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples. By modeling the generative process by which reads are produced from isoforms in RNA-Seq, the MISO model uses Bayesian inference to compute the probability that a read originated from a particular isoform.

::DEVELOPER

Christopher Burge Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

  MISO

:: MORE INFORMATION

Citation

Yarden Katz, Eric T. Wang, Edoardo M. Airoldi*, Christopher B. Burge*.
Analysis and design of RNA sequencing experiments for identifying isoform regulation
Nature Methods 2010 (7, 1009-1015)

RSEG 0.4.9 – Identify Epigenomic Domains from ChIP-Seq data

RSEG 0.4.9

:: DESCRIPTION

The RSEG software package is aimed to analyze ChIP-Seq data, especially for identifying genomic regions and their boundaries marked by diffusive histone modification markers, such as H3K36me3 and H3K27me3. It can work with or without control sample. It can be used to find regions with differential histone modifications patterns, either comparsion between two cell types or between two kinds of histone modifications.

::DEVELOPER

The Smith Lab

:: SCREENSHOTS

Command

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 RSEG

:: MORE INFORMATION

Citation:

Bioinformatics. 2011 Mar 15;27(6):870-1. Epub 2011 Feb 16.
Identifying dispersed epigenomic domains from ChIP-Seq data.
Song Q, Smith AD.

MONKEY 2.0 – Identify Matches to DNA Motifs in Multiple Alignments

MONKEY 2.0

:: DESCRIPTION

MONKEY is a set of programs designed to search alignments of non-coding DNA sequence for matches to matrices representing the sequence specificity of transcription factors.MONKEY employs probabilistic models of factor specificity and binding-site evolution, on which basis we compute the likelihood that putative sites are conserved and assign statistical significance to each hit.

::DEVELOPER

Alan Moses’ Computational Biology Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MONKEY

:: MORE INFORMATION

Citation

Moses et. al.
MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model
Genome Biol. 2004;5(12):R98

PCS 1.5 – Identify and Analyze Conserved K-mers in Pairwise Alignment

PCS 1.5

:: DESCRIPTION

PCS (Pairwise Conservation Scores) is stand-alone pakage to identify and analyze conserved k-mers in pairwise alignment. This program shows high performance for identifying miRNA seed binding sites in 3′-UTRs.

::DEVELOPER

Bioinformatics & Intelligent Information Processing Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX /  Linux
  • Perl 

:: DOWNLOAD

 PCS

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Nov 8;8:432.
Identifications of conserved 7-mers in 3′-UTRs and microRNAs in Drosophila.
Gu J, Fu H, Zhang X, Li Y.

The Virtual Bacterial ID Lab 1.2.3 – Identify deadly Pathogens

The Virtual Bacterial ID Lab 1.2.3

:: DESCRIPTION

Bacterial ID Lab: Use DNA sequencing techniques to identify deadly pathogens.

::DEVELOPER

The Howard Hughes Medical Institute (HHMI)

:: SCREENSHOTS

idlab

:: REQUIREMENTS

  • iPhone /  iPad

:: DOWNLOAD

 The Virtual Bacterial ID Lab

:: MORE INFORMATION

APSampler 3.6.1 – Use Monte Carlo Markov Chain for Identifying of Genetic Background of Complex Diseases

APSampler 3.6.1

:: DESCRIPTION

APSampler is a tool that allows multi-locus and multi-level association analysis of genotypic and phenotypic data. The goal is to find the allelic sets (patterns) that are associated with phenotype. The main difficulty of such a task is, given the multiple loci and multiple alleles, the number of all possible classifiers tends to be extremely large. Therefore, Monte Carlo Markov Chain method is applied to reduce the space of solutions and sample only from regions where it is likely to find a good classifier. Once a set of classifiers is found, there is a problem to validate the results, and this is done using a number of well known methods. In case of single disease level, the resulting classifier divides the space of healthy and ill individuals, and the result is represented in a classic Fisher table. Odds ratio and Fisher’s p-value are calculated if applicable. Also, Kruskal’s gamma and the corresponding p-value can be calculated. After each pattern in the output is described by a p-values set of different multiple-hypothesis corrections, including permutation tests.

::DEVELOPER

Alexander Favorov.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • WIndows / Linux

:: DOWNLOAD

 APSampler

:: MORE INFORMATION

Citation:

Favorov, A.V. et al.
A Markov chain Monte Carlo technique for identification of combinations of allelic variants underlying complex diseases in humans.
Genetics 171, 2113-2121 (2005).

baySeq 2.6.0 – Identify Differential Expressed Genes

baySeq 2.6.0

:: DESCRIPTION

baySeq identifies differential expression in high-throughput ‘count’ data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

::DEVELOPER

Thomas J. Hardcastle

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 baySeq

:: MORE INFORMATION

Citation

Bioinformatics. 2015 Oct 1. pii: btv569.
Generalised empirical Bayesian methods for discovery of differential data in high-throughput biology.
Hardcastle TJ

BMC Bioinformatics. 2010 Aug 10;11:422. doi: 10.1186/1471-2105-11-422.
baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.
Hardcastle TJ, Kelly KA.

COSEG 0.2.2 – Identifies Repeat Subfamilies

COSEG 0.2.2

:: DESCRIPTION

COSEG is a program which automatically identifies repeat subfamilies using significant co-segregating ( 2-3 bp ) mutations.

::DEVELOPER

COSEG Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 COSEG

:: MORE INFORMATION

GeneValidator 1.5.6 – Identify Problems with Predicted Genes

GeneValidator 1.5.6

:: DESCRIPTION

GeneValidator is a tool to identify problematic gene predictions based on comparisons between gene predictions and similar sequences in public databases (e.g., SwissProt).

GeneValidator Web Server

::DEVELOPER

Wurm Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Ruby

:: DOWNLOAD

 GeneValidator

:: MORE INFORMATION

Citation

GeneValidator: identify problems with protein-coding gene predictions.
Drăgan MA, Moghul I, Priyam A, Bustos C, Wurm Y.
Bioinformatics. 2016 Jan 18. pii: btw015