HARSH 0.21 – Haplotype Inference using Reference and Sequencing Data

HARSH 0.21

:: DESCRIPTION

HARSH (HAplotype inference using Reference and Sequencing tecHnology) is a method to infer the haplotype using haplotype reference panel and high throughput sequencing data. It is based on a novel probabilistic model and Gibbs sampler method.

::DEVELOPER

ZarLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ Windows/ MacOsX
Python

:: DOWNLOAD

HARSH

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Sep 15;29(18):2245-52. doi: 10.1093/bioinformatics/btt386.
Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data.
Yang WY, Hormozdiari F, Wang Z, He D, Pasaniuc B, Eskin E.

TIgGER 0.4.0 – Infers Novel Immunoglobulin Alleles from Sequencing Data

TIgGER 0.4.0

:: DESCRIPTION

TIgGER (Tool for Ig Genotype Elucidation via airR-seq) is a set of methods for identifying novel V gene alleles and constructing subject-specific genotypes.

::DEVELOPER

Kleinstein Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
R

:: DOWNLOAD

TIgGER

:: MORE INFORMATION

Citation

Proc Natl Acad Sci U S A, 112 (8), E862-70 2015 Feb 24
Automated Analysis of High-Throughput B-cell Sequencing Data Reveals a High Frequency of Novel Immunoglobulin V Gene Segment Alleles
Gadala-Maria D, Yaari G, Uduman M, Kleinstein SH.

npSeq 1.1.1 – Significance Analysis of Sequencing data

npSeq 1.1.1

:: DESCRIPTION

npSeq is an R package for the significance analysis of sequencing data. The statistic used by npSeq is exactly the same as that in SAM 4.0. The only difference is that npSeq uses symmetric cutoffs, while SAM uses asymmetric cutoffs. Therefore, for some datasets, all significant genes obtained by SAM are either all up-regulated or all down-regulated, but npSeq almost always gives significant genes that include both up-regulated genes and down-regulated genes.

::DEVELOPER

Jun Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

MacOsX/ Linux / WIndows
R Package

:: DOWNLOAD

npSeq

:: MORE INFORMATION

Citation

Jun Li and Robert Tibshirani (2011)
Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data.
Stat Methods Med Res. 2011 Nov 28.

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).

VARIFI – Variant Identification, Filtering and Annotation of Amplicon Sequencing Data

VARIFI

:: DESCRIPTION

VARIFI is a pipeline for finding reliable genetic variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (indels)).

::DEVELOPER

the Center of Integrative Bioinformatics Vienna (CIBIV)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

VARIFI-Web-Based Automatic Variant Identification, Filtering and Annotation of Amplicon Sequencing Data.
Krunic M, Venhuizen P, Müllauer L, Kaserer B, von Haeseler A.
J Pers Med. 2019 Feb 1;9(1). pii: E10. doi: 10.3390/jpm9010010.

A program suite for Filtering False Somatic Mutations from FFPE Tumor Sequencing data

Filtering False Somatic Mutations from FFPE Tumor Sequencing data

:: DESCRIPTION

This program suite consists of 4 PERL programs made to identify and remove potential false positive somatic variants caused by FFPE (formalin-fixed paraffin-embedded) DNA damage on next-generation sequencing data.

::DEVELOPER

the Division of Genome Information Sciences, UCSD

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ WIndows/ MacOsX
Perl

:: DOWNLOAD

program suite

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2012 Aug;40(14):e107. Epub 2012 Apr 6.
Identification of high-confidence somatic mutations in whole genome sequence of formalin-fixed breast cancer specimens.
Yost SE, Smith EN, Schwab RB, Bao L, Jung H, Wang X, Voest E, Pierce JP, Messer K, Parker BA, Harismendy O, Frazer KA.

EBCall – Empirical Bayesian framework for Mutation Detection from Cancer Genome Sequencing data

EBCall

:: DESCRIPTION

EBCall (Empirical Bayesian mutation Calling) is a software package for somatic mutation detection (including InDels). EBCall uses not only paired tumor/normal sequence data of a target sample, but also multiple non-paired normal reference samples for evaluating distribution of sequencing errors, which leads to an accurate mutaiton detection even in case of low sequencing depths and low allele frequencies.

::DEVELOPER

Yuichi Shiraishi (yshira@hgc.jp) ,Kenichi Chiba

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
samtools
R package
C++ Compiler
The VGAM package for R

:: DOWNLOAD

EBCall

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2013 Apr;41(7):e89. doi: 10.1093/nar/gkt126. Epub 2013 Mar 6.
An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data.
Shiraishi Y, Sato Y, Chiba K, Okuno Y, Nagata Y, Yoshida K, Shiba N, Hayashi Y, Kume H, Homma Y, Sanada M, Ogawa S, Miyano S.

PoissonSeq 1.1.2 – Significance Analysis of Sequencing data based on a Poisson log linear model

PoissonSeq 1.1.2

:: DESCRIPTION

PoissonSeq is an R package for the significance analysis of sequencing data based on a Poisson log linear model. This package implements a method for normalization, testing, and false discovery rate estimation for RNA-sequencing data.

::DEVELOPER

Jun Li

:: SCREENSHOTS

N/A

:: REQUIREMENTS

MacOsX/ Linux / WIndows
R Package

:: DOWNLOAD

PoissonSeq

:: MORE INFORMATION

Citation

Jun Li, Daniela M Witten, Iain Johnstone, and Robert Tibshirani (2011)
Normalization, testing, and false discovery rate estimation for rna-sequencing data.
Biostatistics. 2012 Jul;13(3):523-38. Epub 2011 Oct 14.

ContEst 1.0.24530 – Estimate Contamination Level in Sequencing data

ContEst 1.0.24530

:: DESCRIPTION

ContEst is a tool (and method) for estimating the amount of cross-sample contamination in next generation sequencing data. Using a Bayesian framework, contamination levels are estimated from array based genotypes and sequencing reads.

::DEVELOPER

The Cancer Genome Analysis (CGA) group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Mac OsX / Windows
Java

:: DOWNLOAD

ContEst

:: MORE INFORMATION

Citation

Kristian Cibulskis, Aaron McKenna, Tim Fennel, Eric Banks, Mark DePristo and Gad Getz
ContEst: estimating cross-contamination of human samples in next-generation sequencing data
Bioinformatics (2011) 27 (18): 2601-2602.

DGE-EM 1.00 – Accurate Estimation of Gene Expression Levels from DGE Sequencing Data

DGE-EM 1.00

:: DESCRIPTION

DGE-EM package can be used to infer gene expression levels from 3′-tag Digital Gene Expression (DGE) data. DGE-EM uses a novel expectation-maximization algorithm that takes into account alternative splicing isoforms and tags that map at multiple locations in the genome, and corrects for incomplete digestion and sequencing errors. Experimental results on real DGE data generated from reference RNA samples show that our algorithm outperforms commonly used estimation methods based on unique tag counting as well as estimates obtained from RNA-Seq data for the same samples. Results of a comprehensive simulation study assessing the effect of various experimental parameters suggest that further improvements in estimation accuracy could be achieved by optimizing protocol parameters such as the anchoring enzymes and digestion probability.

::DEVELOPER

Bioinformatics Lab , Computer Science & Engineering Dept. University of Connecticut

:: SCREENSHOTS

Command Line

:: REQUIREMENTS

Windows / Linux / Mac OsX
Java

:: DOWNLOAD

DGE-EM

:: MORE INFORMATION

Citation:

M. Nicolae and I.I. Mandoiu,
Accurate Estimation of Gene Expression Levels from DGE Sequencing Data,
Invited talk, 1st Annual RECOMB Satellite Workshop on Massively Parallel Sequencing, March 26-27, 2011,