baySeq 2.26.0 – Identify Differential Expressed Genes

baySeq 2.26.0

:: 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.

SNPYGoat 1.0 – Identify Several Goat Y-chromosomal Haplotypes

SNPYGoat 1.0

:: DESCRIPTION

SNPYGoat Software allows users of the SNPYGoat multiplex system to rapidly identify several goat Y-chromosomal haplotypes  Y1A, Y1B, Y1C and Y2 by automatically comparing the obtained profile with a reference database.

::DEVELOPER

Filipe Pereira

:: SCREENSHOTS

: REQUIREMENTS

  • Windows

:: DOWNLOAD

SNPYGoat

:: MORE INFORMATION

Citation

Pereira F, Carneiro J, Soares P, Maciel S, Nejmeddine F, Lenstra JA, Gusm?o L, Amorim A
A multiplex primer extension assay for the rapid identification of paternal lineages in domestic goat (Capra hircus): laying the foundations for a detailed caprine Y chromosome phylogeny
Molecular Phylogenetics and Evolution. 2008. 49:663-668

OMiMa – Identify Functional Motifs in DNA or Protein Sequences

OMiMa

:: DESCRIPTION

The OMiMa (the Optimized Mixture Markov model) System is a computational tool for identifying functional motifs in DNA or protein sequences. OMiMa System is based on the Optimized Mixture of Markov models that are able to incorporate most dependencies within a motif. Most important, OMiMa is capable to adjust model complexity according to motif dependency structures, so it can minimize model complexity without compromising prediction accuracy. OMiMa uses our fast Markov chain optimization method, the Directed Neighbor-Joining (DNJ), which makes OMiMa more computationally efficent.

::DEVELOPER

OMiMa team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 OMiMa

:: MORE INFORMATION

Citation

Weichun Huang, David M Umbach, Uwe Ohler, Leping Li.
Optimized mixed Markov models for motif identification.
BMC Bioinformatics 2006, 7:279

LaneRuler 1.1 – Identify Lanes in Gel Image

LaneRuler 1.1

:: DESCRIPTION

LaneRuler will identify lanes in a gel image. The lanes on such gels may not be straight and parallel due to various reasons, and these deviations must be accounted for in order to accurately size the restriction fragments in each lane. In order to meet the high throughput requirements by the projects at the British Columbia Cancer Agency Genome Sciences Center (GSC), the software has capability to verify and correct its results automatically, and prompting for user inspection only for extremely abnormal cases. In validation testing using Bacterial Artificial Chromosome (BAC) clones, the automatic lane tracking results gave restriction fragment sizing results that are comparable to those from manually supervised lane tracking results, achieving sensitivity and specificity of restriction fragment identification exceeding 95%. The current conception of the program is able to successfully process 96% of the gels with no human intervention.

::DEVELOPER

Canada’s Michael Smith Genome Sciences Centre

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / Mac /  Linux
  • Java

:: DOWNLOAD

 LaneRuler

:: MORE INFORMATION

Citation

Wong, R.T.F.; Flibotte, S.; Corbett, R.; Saeedi, P.; Jones, S.J.M.; Marra, M.A.; Schein, J.E.; Birol, I.; (2010 )
LaneRuler: Automated Lane Tracking for DNA Electrophoresis Gel Images
Automation Science and Engineering, IEEE Transactions Volume: 7 Issue:3 706 – 708

MapDamage 2.2.1 – Identifies and Quantifies DNA Damage Patterns in Ancient DNA

MapDamage 2.2.1

:: DESCRIPTION

mapDamage is a computational framework written in Python and R, which tracks and quantifies DNA damage patterns among ancient DNA sequencing reads generated by Next-Generation Sequencing platforms.

::DEVELOPER

The Centre for Anthropobiology and Genomics of Toulouse

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/MacOsX/Linux
  • Python
  • R package

:: DOWNLOAD

 MapDamage

:: MORE INFORMATION

Citation

Jónsson H, Ginolhac A, Schubert M, Johnson P, Orlando L.
mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters.
Bioinformatics (2013) 29 (13): 1682-1684. doi: 10.1093/bioinformatics/btt193

TIGER 1.02 – Identify Rapidly-evolving Characters in Evolutionary Data

TIGER 1.02

:: DESCRIPTION

TIGER is open source software for identifying rapidly evolving sites (columns in an alignment, or characters in a morphological dataset). It can deal with many kinds of data (molecular, morphological etc.). Sites like these are important to identify as they are very often removed or reweighted in order to improve phylogenetic reconstruction. When a site is changing very quickly between taxa it might not hold much phylogenetic information and therefore might simply be a source of noise. Use of TIGER can (a) allow you to see the amount of rapid evolution and noise in your alignment and (b) provide a quick and easy way to remove as many of the “noisy” sites as possible.

::DEVELOPER

McInerney lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • Python

:: DOWNLOAD

TIGER

:: MORE INFORMATION

Citation:

Cummins, C.A. and McInerney, J.O. (2011)
A method for inferring the rate of evolution of homologous characters that can potentially improve phylogenetic inference, resolve deep divergence and correct systematic biases.
Systematic Biology 60 (6) 833-844.

DIME 1.2 – Identifying Differential ChIP-seq Based on an Ensemble of Mixture Models

DIME 1.2

:: DESCRIPTION

DIME (Differential Identification using Mixtures Ensemble) is an ensemble of methods for differential analysis. Specifically, it considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) for analyzing ChIP-seq data comparing two samples. This package can also be used to identify differential in other high throughput data such as microarray and DNA methylation.

::DEVELOPER

Statistical Genetics and Bioinformatics Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 DIME

:: MORE INFORMATION

Citation

Taslim, C., Huang, T., and Lin, S. (2011).
DIME: R-package for Identifying Differential ChIP-seq Based on an Ensemble of Mixture Models.
Bioinformatics, 27, 1569-1570.

fineSTRUCTURE 4.0.1 – Identify Population Structure using Dense Sequencing Data

fineSTRUCTURE 4.0.1

:: DESCRIPTION

fineSTRUCTURE is a fast and powerful algorithm for identifying population structure using dense sequencing data.  By using the output of ChromoPainter as a (nearly) sufficient summary statistic, it is able to perform model-based Bayesian clustering on large datasets, including full resequencing data, and can handle up to 1000s of individuals.

::DEVELOPER

Daniel Lawson

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows with  MinGW/ MacOsX

:: DOWNLOAD

  fineSTRUCTURE

:: MORE INFORMATION

Citation

Lawson, Hellenthal, Myers, and Falush (2012),
Inference of population structure using dense haplotype data“,
PLoS Genetics, 8 (e1002453).