CNIT 5.1 – Copy Number Inferring tool

CNIT 5.1

:: DESCRIPTION

CNIT is designed for Affymetrix GeneChip to analyze copy number of each SNP allele. CNIT can be applicable in chromosome-abnormal disease, cancer and copy number variation studies, and can provide accurate CN estimations with low false-positive rate.

::DEVELOPER

Cathy S.J. Fann lab,Institute of Biomedical Informatics, National Yang-Ming University, Taipei

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R package

:: DOWNLOAD

 CNIT

:: MORE INFORMATION

Citation

Genome-wide copy number analysis using copy number inferring tool (CNIT) and DNA pooling.
Lin CH, Huang MC, Li LH, Wu JY, Chen YT, Fann CS.
Hum Mutat. 2008 Aug;29(8):1055-62

IPPI – Inferring Protein-Protein Interactions for YEAST

IPPI

:: DESCRIPTION

IPPI is a web server of inferring protein-protein interactions

::DEVELOPER

Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation:

Bioinformatics. 2003 Oct;19 Suppl 2:ii58-65.
Inferring strengths of protein-protein interactions from experimental data using linear programming.
Hayashida M, Ueda N, Akutsu T.

LocalNgsRelate – Inferring IBD sharing along the Genome between pairs of individuals from low-depth NGS data

LocalNgsRelate

:: DESCRIPTION

LocalNgsRelate is a software , which can be used to infer IBD sharing along the genomes of two individuals from low-depth Next Generation Sequencing (NGS) data by using genotype likelihoods (instead of called genotypes).

::DEVELOPER

LocalNgsRelate team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

LocalNgsRelate

:: MORE INFORMATION

Citation

Severson AL, Korneliussen TS, Moltke I.
LocalNgsRelate: a software tool for inferring IBD sharing along the genome between pairs of individuals from low-depth NGS data.
Bioinformatics. 2021 Oct 28:btab732. doi: 10.1093/bioinformatics/btab732. Epub ahead of print. PMID: 34718411.

PoolHap2 / PoolHapX 1.0.0 – Inferring Haplotype frequencies from Pooled Sequencing

PoolHap2 / PoolHapX 1.0.0

:: DESCRIPTION

PoolHap2 is a computer program that infers haplotype (or epitype in case of DNA methylation sequencing) from a pool.  In its current release, only pathogen sequencing application is implemented. In pathogen studies utilizing next generation sequencing, investigators ofte n collect samples naturally as pools of multiple strains, e.g., when the samples are taken from patients’ blood. To analyze these types of within-host polymorphisms, one would ideally like to determine the haplotypes in the sample. Even though isolation of single strains is possible by time-consuming experiments, the haplotype frequencies of different pathogen strains within a host are usually unknown, and this may well alter the initial within-sample frequencies. Here we developed Poolhap, a tool enabling researchers to infer the strain numbers and haplotype frequencies in silico from sequences of pooled samples.

The PoolHapX program reconstructs haplotypes within-host from pooled-sequencing data by integrating population genetic models (statistical linkage disequilibrium) with genomics reads (physical linkage). It approximate the resolution of single-cell sequencing using only pooled sequencing data, enabling within-host evolution analyses.

::DEVELOPER

Quan Long

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows / MacOsX
  • Java

:: DOWNLOAD

 PoolHap , PoolHapX

:: MORE INFORMATION

Citation

Cao C, He J, Mak L, Perera D, Kwok D, Wang J, Li M, Mourier T, Gavriliuc S, Greenberg M, Morrissy AS, Sycuro LK, Yang G, Jeffares DC, Long Q.
Reconstruction of Microbial Haplotypes by Integration of Statistical and Physical Linkage in Scaffolding.
Mol Biol Evol. 2021 May 19;38(6):2660-2672. doi: 10.1093/molbev/msab037. PMID: 33547786; PMCID: PMC8136496.

PLoS One. 2011 Jan 5;6(1):e15292. doi: 10.1371/journal.pone.0015292.
PoolHap: inferring haplotype frequencies from pooled samples by next generation sequencing.
Long Q, Jeffares DC, Zhang Q, Ye K, Nizhynska V, Ning Z, Tyler-Smith C, Nordborg M.

NARROMI – Inferring Gene Regulatory Networks from Gene Expression data

NARROMI

:: DESCRIPTION

NARROMI is a MATLAB program for inferring gene regulatory networks from gene expression data. It is a novel method combining ordinary differential equation based recursive optimization (RO) and information-theory based mutual information (MI).

::DEVELOPER

Zhao Group at the Tongji University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX
  • MatLab

:: DOWNLOAD

 NARROMI

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jan 1;29(1):106-13. doi: 10.1093/bioinformatics/bts619. Epub 2012 Oct 18.
NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference.
Zhang X1, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, Hao JK, Chen L.

CMI2NI – Inferring Gene Regulatory Networks from Gene Expression data

CMI2NI

:: DESCRIPTION

CMI2NI (CMI2-based network inference) is a software for inferring gene regulatory networks from gene expression data. It is a novel method using a new proposed concept of Conditional Mutual Inclusive Information (CMI2) which can accurately measure direct dependences between genes. Given the small size samples of gene expression data, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the dependence or regulation strength between genes.

::DEVELOPER

ZhaoGroup at the Tongji University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux / Mac OsX
  • MatLab

:: DOWNLOAD

  CMI2NI

:: MORE INFORMATION

Citation

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.
Zhang X, Zhao J, Hao JK, Zhao XM, Chen L.
Nucleic Acids Res. 2014 Dec 24. pii: gku1315.

SNPWEIGHTS 2.1 – Inferring Genome-wide Genetic Ancestry using SNP Weights

SNPWEIGHTS 2.1

:: DESCRIPTION

SNPweights is a software package for inferring genome-wide genetic ancestry using SNP weights precomputed from large external reference panels

::DEVELOPER

Alkes Price

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 SNPweights

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jun 1;29(11):1399-406. doi: 10.1093/bioinformatics/btt144. Epub 2013 Mar 28.
Improved ancestry inference using weights from external reference panels.
Chen CY, Pollack S, Hunter DJ, Hirschhorn JN, Kraft P, Price AL.

DynaDup 2.3.2 – Inferring Optimal Species Trees under Gene Duplication and Loss

DynaDup 2.3.2

:: DESCRIPTION

DynaDup is a Dynamic Programing based Java application for building species trees from gene trees minimizing gene duplication and gene duplication and loss.

::DEVELOPER

The Warnow Lab 

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /Windows/MacOsX
  • Java

:: DOWNLOAD

 DynaDup

:: MORE INFORMATION

Citation

Pac Symp Biocomput. 2013:250-61.
Inferring optimal species trees under gene duplication and loss.
Bayzid MS1, Mirarab S, Warnow T.

LOX 1.8beta – Inferring Level of Expression from Diverse Methods of Census Sequencing

LOX 1.8beta

:: DESCRIPTION

LOX (Level Of eXpression) is a program that employs Markov Chain Monte Carlo to estimate level of expression from census sequencing data sets with multiple treatments or samples.

::DEVELOPER

the Townsend Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOsX
  • C++Compiler

:: DOWNLOAD

 LOX

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Aug 1;26(15):1918-9. Epub 2010 Jun 10.
LOX: inferring Level Of eXpression from diverse methods of census sequencing.
Zhang Z, López-Giráldez F, Townsend JP.

GeneNet 1.2.14 – Modeling and Inferring Gene Networks

GeneNet 1.2.14

:: DESCRIPTION

GeneNet is a package for analyzing gene expression (time series) data with focus on the inference of gene networks.

::DEVELOPER

Strimmer Lab

:: SCREENSHOTS

GeneNet

:: REQUIREMENTS

  • Windows / Linux / MacOSX
  • R package

:: DOWNLOAD

 GeneNet

:: MORE INFORMATION

Citation

BMC Syst Biol. 2007 Aug 6;1:37.
From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data.
Opgen-Rhein R, Strimmer K.