BOL: Related items

Google Genomics

Tenzin Paul — Thu, 18 Dec 2014 11:05:42 -0600

Explore genetic variation interactively. Compare entire cohorts in seconds with SQL-like queries. Compute transition/transversion ratios, genome-wide association, allelic frequency and more.
Process big genomic data easily. Run batch analyses like principal component analysis and Hardy-Weinberg equilibrium on as many samples as you like, in minutes or hours, with just a little code.
Use Google's infrastructure and big data expertise. Store one genome or a million using Google Genomics and take advantage of the same infrastructure that powers Search, Maps, YouTube, Gmail and Drive.
Support emerging global standards. Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.

Address of the bookmark: https://cloud.google.com/genomics/

Alignment of closely related whole genomes/scaffolds

Rahul Nayak — Fri, 29 Jan 2016 10:37:27 -0600

With the relative ease and low cost of current generation sequencing technologies has led to a dramatic increase in the number of sequenced genomes for species across the tree of life. This increasing volume of data requires tools that can quickly compare multiple whole-genome sequences, millions of base pairs in length, to aid in the study of populations, pan-genomes, and genome evolution.This bookmaks have been created to report new tools for whole genome alignments.

Please report new whole genome alignment tools under comment sections.

Address of the bookmark: http://www.cs.utoronto.ca/~brudno/721.full.pdf

seqloc 0.6

Gudiya Pal — Sun, 28 Dec 2014 12:51:29 -0600

The Bio.SeqLoc modules in seqloc are designed to represent positions and locations (ranges of positions) on sequences, particularly nucleotide sequences. My original motivation for writing these packages was handing the locations of genes in eukaryotic genomes.

Handle sequence locations for bioinformatics http://www.ingolia-lab.org/seqloc-tutorial.html

Address of the bookmark: http://www.stackage.org/snapshot/nightly-2014-12-28/package/seqloc-0.6

MeDuSa: a multi-draft based scaffolder

Abhimanyu Singh — Wed, 14 Feb 2018 02:49:00 -0600

MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalises the scaffolding problem by means of a combinatorial optimisation formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries.

Address of the bookmark: https://github.com/combogenomics/medusa

LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)

Jit — Mon, 14 May 2018 04:26:30 -0500

LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly.

Further information about LACHESIS, including source code, documentation and a user's guide are available at: http://shendurelab.github.io/LACHESIS.

Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology 2013 Dec;31(12):1119-25. doi: 10.1038/nbt.272. PubMed PMID: 24185095.

http://shendurelab.github.io/LACHESIS/

Address of the bookmark: http://shendurelab.github.io/LACHESIS/

ComparativeGenomics Exercise2

Neel — Wed, 22 Aug 2018 22:10:56 -0500

COMPARATIVE MICROBIAL GENOMICS ANALYSIS WORKSHOP @ cbs.dtu.dk

Free Bioinformatics workbench https://www.mn.uio.no/ifi/english/research/networks/clsi/earlier_seminars/2012/tammivesth_osloseminarfinal.pdf

Genome assembly tutorial "Genome Assembly for short and long reads"

Jit — Sat, 19 Jan 2019 17:29:53 -0600

In this lab we will perform de novo genome assembly of a bacterial genome. You will be guided through the genome assembly starting with data quality control, through to building contigs and analysis of the results. At the end of the lab you will know:

How to perform basic quality checks on the input data
How to run a short read assembler on Illumina data
How to run a long read assembler on Pacific Biosciences or Oxford Nanopore data
How to improve the accuracy of a long read assembly using short reads
How to assess the quality of an assembly

https://bioinformaticsdotca.github.io/high-throughput_biology_2017

Address of the bookmark: https://bioinformaticsdotca.github.io/high-throughput_biology_2017_module6_lab

RaGOO: Fast Reference-Guided Scaffolding of Genome Assembly Contigs

Jit — Sun, 27 Oct 2019 00:57:23 -0500

Alonge M, Soyk S, Ramakrishnan S, Wang X, Goodwin S, Sedlazeck FJ, Lippman ZB, Schatz MC: Fast and accurate reference-guided scaffolding of draft genomes. bioRxiv 2019.

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
Intact ordering and orienting of contigs.
Misassembly correction
GFF lift-over
Structural variant calling with and integrated version of Assemblytics
Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

Tools to access the quality of your assembled genome !

LEGE — Thu, 08 Aug 2024 23:31:18 -0500

FASTA VALIDATOR + SEQKIT RMDUP: FASTA validation
GENOMETOOLS GT GFF3VALIDATOR: GFF3 validation
ASSEMBLATHON STATS: Assembly statistics
GENOMETOOLS GT STAT: Annotation statistics
NCBI FCS ADAPTOR: Adaptor contamination pass/fail
NCBI FCS GX: Foreign organism contamination pass/fail
BUSCO: Gene-space completeness estimation
TIDK: Telomere repeat identification
LAI: Continuity of repetitive sequences
KRAKEN2: Taxonomy classification
HIC CONTACT MAP: Alignment and visualisation of HiC data
MUMMER → CIRCOS + DOTPLOT & MINIMAP2 → PLOTSR: Synteny analysis
MERQURY: K-mer completeness, consensus quality and phasing assessment

MashMap: a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s)

Jit — Tue, 12 Dec 2017 17:23:31 -0600

MashMap is a fast and approximate software for mapping long reads (PacBio/ONT) or assembly to reference genome(s). It maps a query sequence against a reference region if and only if its estimated alignment identity is above a specified threshold. It does not compute the alignments explicitly, but rather estimates a k-mer based Jaccard similarity using a combination of Winnowing and MinHash. This is then converted to an estimate of sequence identity using the Mash distance. An appropriate k-mer sampling rate is automatically determined given minimum local alignment length and identity thresholds. The efficiency of the algorithm improves as both of these thresholds are increased.

Address of the bookmark: https://github.com/marbl/MashMap