BOL: Related items

Algorithms for DNA Sequencing (course offered each month)

Rahul Agarwal — Sun, 26 Jul 2015 01:57:02 -0500

"We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets."

Source : https://www.coursera.org/course/ads1

Address of the bookmark: https://www.coursera.org/course/ads1

Pevzner Lab !

Thu, 02 Nov 2023 05:39:26 -0500

The laboratory works on genome sequencing, immunoproteogenomics, antibiotics sequencing, and comparative genomics - computational technologies that enabled new applications and allowed scientists to attack biological problems that remained beyond the reach of previous techniques.

https://bioalgorithms.ucsd.edu/research4.html

HiCdat

Jit — Fri, 12 Feb 2016 05:23:44 -0600

HiCdat: a fast and easy-to-use Hi-C data analysis tool

HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.

More at http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0678-x

Address of the bookmark: https://github.com/MWSchmid/HiCdat

KisSplice

Jit — Tue, 16 Aug 2016 08:34:19 -0500

KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.

KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.

KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.

Address of the bookmark: http://kissplice.prabi.fr/

Machine Learning !!!

Gudiya Pal — Fri, 01 Jul 2016 12:57:12 -0500

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

WiseScaffolder

Poonam Mahapatra — Wed, 13 Jul 2016 08:08:57 -0500

Function

WiseScaffolder is a stand-alone semi-automatic application for genome scaffolding of pre-assembled contigs using mate-pair data. It also produces editable scaffold maps, allowing either to build gapped scaffolds or usable as a common thread for the manual improvement of scaffolds.

Description

WiseScaffolder includes 4 subcommands: dumpconfig generates a configuration file that notably specifies the average insert size of the mate-pair library preprocess allows the detection and correction of chimerae, the estimation of contigs copy number and produces valuable outputs for the manual improvement of scaffolds scaffold constitutes the central scaffold-builder and comprises two modules:

i) the interative_scaffold_extender, which works with big, unambiguous contigs, or when they run out, single copy contigs, and

ii) the small_contig_inserter, which inserts the small contigs within scaffolds buildfasta converts the scaffold(s) map(s) into Fasta sequences.

Address of the bookmark: http://abims.sb-roscoff.fr/wisescaffolder

CrossMap

Abhimanyu Singh — Mon, 05 Sep 2016 04:07:38 -0500

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)).
It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF.
CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome.
We do not recommend using CrossMap to convert genome coordinates between species.

Address of the bookmark: http://crossmap.sourceforge.net/

TEannot

Jit — Thu, 18 Aug 2016 10:02:03 -0500

We advise to run first the TEdenovo pipeline but it is not compulsory. We suppose you begin by running the TEannot pipeline on the example provided in the directory "db/" rather than directly on your own genomic sequences. Thus, from now on, the project name is "DmelChr4".

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

LUMPY

Shruti Paniwala — Thu, 25 Aug 2016 08:05:02 -0500

A probabilistic framework for structural variant discovery.

Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6): R84. doi:10.1186/gb-2014-15-6-r84.

More at https://github.com/arq5x/lumpy-sv

Address of the bookmark: https://github.com/arq5x/lumpy-sv

Ka, Ks and Ka/Ks calculations

Poonam Mahapatra — Mon, 29 Aug 2016 11:44:11 -0500

gKaKs is a codon-based genome-level Ka/Ks computation pipeline developed and based on programs from four widely used packages: BLAT, BLASTALL (including bl2seq, formatdb and fastacmd), PAML (including codeml and yn00) and KaKs_Calculator (including 10 substitution rate estimation methods). gKaKs can automatically detect and eliminate frameshift mutations and premature stop codons to compute the substitution rates (Ka, Ks and Ka/Ks) between a well-annotated genome and a non-annotated genome or even a poorly assembled scaffold dataset. It is especially useful for newly sequenced genomes that have not been well annotated.

Look for KaKs calculation:

https://github.com/fumba/kaks-calculator

http://longlab.uchicago.edu/?q=gKaKs

http://www.ncbi.nlm.nih.gov/pubmed/23314322

Address of the bookmark: http://longlab.uchicago.edu/?q=gKaKs