WEAV 0.2 – de novo Assembly program for both Genome and RNA

WEAV 0.2

:: DESCRIPTION

WEAV wants to be general sequence assembler including genomic sequence assembler, whole transcriptome assembler, metagenomic sequence assembler, and so on.

::DEVELOPER

Ting Chen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 WEAV

:: MORE INFORMATION

MotifIndexer – de novo Promoter Motifs Finding program

MotifIndexer

:: DESCRIPTION

MotifIndexer is a comprehensive strategy for de novo identification of DNA regulatory motifs at a genome level.

::DEVELOPER

Dinesh-Kumar Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 MotifIndexer

:: MORE INFORMATION

Citation

Ma S, Bachan S, Porto M, Bohnert HJ, Snyder M and Dinesh-Kumar SP. (2012)
Discovery of stress responsive DNA regulatory motifs in Arabidopsis.
PLoS ONE 7:e43198

Multi-Dendrix 1.0 – Multiple Pathway De novo Driver Exclusivity

Multi-Dendrix 1.0

:: DESCRIPTION

Multi-Dendrix is an algorithm for the simultaneous discovery of multiple driver pathways using only somatic mutation data from a cohort of samples. Multi-Dendrix uses an integer linear program to identify pathway sets such that each pathway contains genes with approximately mutually exclusive mutations and high coverage of the sample set.

::DEVELOPER

Raphael Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • Python

:: DOWNLOAD

   Multi-Dendrix

:: MORE INFORMATION

Citation:

M.D.M. Leiserson, D. Blokh, R. Sharan, B.J. Raphael. (2013)
Simultaneous Identification of Multiple Driver Pathways in Cancer.
PLoS Comp Bio, 9(5):e1003054

MetaSPS 7.332 – De novo Protein Sequencing

MetaSPS 7.332

:: DESCRIPTION

MetaSPS was shown to yield the longest and most accurate de novo sequences from tandem mass spectrometry data at nearly full sequence coverage.

::DEVELOPER

Nuno BandeiraAdrian Guthals [aguthals (at) cs.ucsd.edu] ,CCMS The Center for Computational Mass Spectrometry

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX

:: DOWNLOAD

 MetaSPS

:: MORE INFORMATION

Citation

Mol Cell Proteomics. 2012 Oct;11(10):1084-96. Epub 2012 Jul 13.
Shotgun protein sequencing with meta-contig assembly.
Guthals A1, Clauser KR, Bandeira N.

UniNovo 20130520 – Universal tool for de novo Peptide Sequencing

UniNovo 20130520

:: DESCRIPTION

UniNovo is a universal de novo peptide sequencing tool that works well for various types of spectra and spectral pairs (e.g., CID, ETD, HCD, CID/ETD, etc). The accuracy of de novo reconstructions generated by UniNovo is better than or comparable to PepNovo+ or PEAKS. Moreover, UniNovo also estimates the error rate of the reported reconstruction.

::DEVELOPER

CCMS The Center for Computational Mass Spectrometry

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ Windows/ MacOsX
  • Java

:: DOWNLOAD

 UniNovo

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Aug 15;29(16):1953-62. doi: 10.1093/bioinformatics/btt338. Epub 2013 Jun 12.
UniNovo: a universal tool for de novo peptide sequencing.
Jeong K1, Kim S, Pevzner PA.

Tedna 1.3 – Transposable Element De Novo Assembler

Tedna 1.3

:: DESCRIPTION

Tedna is a lightweight de novo transposable element assembler. It assembles the transposable elements directly from the raw reads.

::DEVELOPER

INRA, URGI

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • C++ Compiler

:: DOWNLOAD

 Tedna

:: MORE INFORMATION

Citation

Tedna: a Transposable Element De Novo Assembler.
Zytnicki M, Akhunov E, Quesneville H.
Bioinformatics. 2014 Jun 3. pii: btu365.

NPLB 1.0.0 – Learn de novo Promoter Architectures from Genome-wide TSSs

NPLB 1.0.0

:: DESCRIPTION

NPLB is an efficient, organism-independent method for characterizing such diverse architectures directly from experimentally identified genome-wide TSSs, without relying on known promoter elements.

::DEVELOPER

NPLB team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX
  • Python

:: DOWNLOAD

 NPLB

:: MORE INFORMATION

Citation

No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites.
Mitra S, Narlikar L.
Bioinformatics. 2015 Nov 2. pii: btv645.

Famdenovo 0.1.1 – Calculating the probability of being de novo for a Genetic Mutation using family history data

Famdenovo 0.1.1

:: DESCRIPTION

Famdenovo is an algorithm that calculates the probability of de novo status in deleterious germline mutations using family history data.

::DEVELOPER

Statistical Bioinformatics Lab, The University of Texas M. D. Anderson Cancer Center

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux /MacOsX
  • R package

:: DOWNLOAD

Famdenovo

:: MORE INFORMATION

Citation

Gao F, Pan X, Dodd-Eaton EB, Recio CV, Montierth MD, Bojadzieva J, Mai PL, Zelley K, Johnson VE, Braun D, Nichols KE, Garber JE, Savage SA, Strong LC, Wang W.
A pedigree-based prediction model identifies carriers of deleterious de novo mutations in families with Li-Fraumeni syndrome.
Genome Res. 2020 Aug;30(8):1170-1180. doi: 10.1101/gr.249599.119. Epub 2020 Aug 18. PMID: 32817165; PMCID: PMC7462073.

Anchor 0.3.1 – Post-processing Tools for de novo Assemblies

Anchor 0.3.1

:: DESCRIPTION

Anchor is a set of tools for making automated improvements to de novo assemblies.

Anchor currently includes two main modules:

– Correction of erroneous single-nucleotide variants and small indels
– Scaffold-filling by local re-assembly

::DEVELOPER

Canada’s Michael Smith Genome Sciences Centre

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 Anchor

:: MORE INFORMATION

TBnovo – De novo Protein Sequencing by combining Top-down and Bottom-up Mass Spectrometry

TBnovo

:: DESCRIPTION

TBNovo is a de novo protein sequencing software tool which combines both top down and bottom up tandem mass spectra.

::DEVELOPER

Xiaowen Liu ,CCMS The Center for Computational Mass Spectrometry

:: REQUIREMENTS

  • Linux/Windows
  • JRE

:: DOWNLOAD

 TBnovo

:: MORE INFORMATION

Citation

J Proteome Res. 2014 Jul 3;13(7):3241-8. doi: 10.1021/pr401300m. Epub 2014 Jun 18.
De novo protein sequencing by combining top-down and bottom-up tandem mass spectra.
Liu X1, Dekker LJ, Wu S, Vanduijn MM, Luider TM, Tolić N, Kou Q, Dvorkin M, Alexandrova S, Vyatkina K, Paša-Tolić L, Pevzner PA.