BOL: Related items

1mb long DNA with Nanopore technology

Jit — Tue, 19 Dec 2017 18:49:28 -0600

The first continuous DNA read of more than a million bases (>1Mb) has been achieved, using Oxford Nanopore sequencing technology. Congratulations to Martin Smith and collaborators! Read more: http://bit.ly/2j5TNCO

nanofilt: Filtering and trimming of long read sequencing data

Jit — Mon, 30 Jul 2018 12:01:52 -0500

Filtering on quality and/or read length, and optional trimming after passing filters.
Reads from stdin, writes to stdout.

Intended to be used:

directly after fastq extraction
prior to mapping
in a stream between extraction and mapping

https://github.com/wdecoster/nanofilt

Address of the bookmark: https://github.com/wdecoster/nanofilt

Basic Structure of Snakemake Pipeline Run !

Abhi — Thu, 14 Oct 2021 07:01:38 -0500

/user/snakemake-demo$ ls

config.json data envs scripts slurm-240702.out Snakefile

data = mock data for the snakefile to use
Snakefile = name of the snakemake “formula” file
- Note: The default file that snakemake looks for in the current working directory is the Snakefile. If you would like to override that you can specify it following the -s
  - snakemake -s snakefile.py
envs = directory for storing the conda environments that the workflow will use.
scripts = directory for storing python scripts called by the snakemake formula.
config.json = json format file with extra parameters for our snakemake file to use.
cluster.json = json format file with specification for running on the HPC
samples.txt = file we will use later relating to the config.json file.

Run the snakemake file as a dry run (the example workflow shown above).

This will build a DAG of the jobs to be run without actually executing them.
snakemake --dry-run

User can execute rules of interest.

snakemake --dry-run all VS. snakemake --dry-run call VS. snakemake --dry-run bwa

Run the snakemake file in order to produce an image of the DAG of jobs to be run.

snakemake --dag | dot -Tsvg > dag.svg OR snakemake --dag | dot -Tsvg > dag.svg

Run the snakemake (this time not as a dry run)

snakemake --use-conda

shovill: Assemble bacterial isolate genomes from Illumina paired-end reads

BioStar — Sat, 02 Jan 2021 07:05:36 -0600

Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time. Shovill also supports other assemblers like SKESA, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.

Address of the bookmark: https://github.com/tseemann/shovill

DISCO : multi threaded and multiprocess distributed memory overlap-layout-consensus (OLC) metagenome assembler

Jit — Mon, 10 Jul 2017 10:09:27 -0500

DISCO is a multi threaded and multiprocess distributed memory overlap-layout-consensus (OLC) metagenome assembler. Disco was developed as a scalable assembler to assemble large metagenomes from billions of Illumina sequencing reads of complex microbial communities. Disco was parallelized for computer clusters in a hybrid architecture that integrated shared-memory multi-threading, point-to-point message passing, and remote direct memory access. The assembly and scaffolding were performed using an iterative overlap graph approach.

Address of the bookmark: http://disco.omicsbio.org/

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation

Shruti Paniwala — Thu, 26 May 2022 00:59:49 -0500

CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.

Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to maarten.reijnders@unil.ch or create an issue.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075

Address of the bookmark: https://gitlab.com/mreijnders/crowdgo

RITA: Rapid identification of high-confidence taxonomic assignments for metagenomic data

Jit — Mon, 27 Nov 2017 08:25:33 -0600

RITA is a standalone software package and Web server for taxonomic assignment of metagenomic sequence reads. By combining homology predictions from BLAST or UBLAST with compositional classifications from a Naive Bayes classifier, RITA is able to achieve very high accuracy on short reads. Unlike other hybrid approaches which combine these predictions for all sequences to be classified, RITA uses a pipeline to first identify cases where both types of classifier are in agreement, which constitute the highest-confidence set. Sequences not classified in this manner are subjected to a series of downstream classification steps.

This work has been accepted for publication:

MacDonald NJ, Parks DH, and Beiko RG. Rapid identification of taxonomic assignments. Accepted to Nucleic Acids Research April 4, 2012.

If you have any questions or bug reports, please let us know at .

Address of the bookmark: http://kiwi.cs.dal.ca/Software/RITA

BioCircos.js is an open source interactive Javascript library to interactive display biological data on the web

Jit — Fri, 19 Jan 2018 15:03:51 -0600

BioCircos.js is an open source interactive Javascript library which provides an easy way to interactive display biological data on the web. It implements a raster-based SVG visualization using the open source Javascript framework jquery.js. BioCircos.js is multiplatform and works in all major internet browsers (Internet Explorer, Mozilla Firefox, Google Chrome, Safari, Opera). Its speed is determined by the client’s hardware and internet browser. For smoothest user experience, we recommend Google Chrome.

BioCircos.js provides SNP, CNV, HEATMAP, LINK, LINE, SCATTER, ARC, TEXT, and HISTGRAMmodules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides BACKGROUND module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.

Demo

Address of the bookmark: http://bioinfo.ibp.ac.cn/biocircos/document/index.html

AfterQC: Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data

Jit — Fri, 29 Jun 2018 03:26:03 -0500

Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair. Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats The author has reimplemented this tool in C++ with multithreading support to make it much faster. The new tool is called fastp and can be found at: https://github.com/OpenGene/fastp . If you prefer a C++ based tool, please use fastp instead. https://github.com/OpenGene/AfterQC

Address of the bookmark: https://github.com/OpenGene/AfterQC

SeqMonk:A tool to visualise and analyse high throughput mapped sequence data

Jit — Tue, 11 Sep 2018 04:39:38 -0500

SeqMonk is a program to enable the visualisation and analysis of mapped sequence data. It was written for use with mapped next generation sequence data but can in theory be used for any dataset which can be expressed as a series of genomic positions. It's main features are:

Import of mapped data from mapped data (BAM/SAM/bowtie etc)
Creation of data groups for visualisation and analysis
Visualisation of mapped regions against an annotated genome.
Flexible quantitation of the mapped data to allow comparisons between data sets
Statistical analysis of data to find regions of interest
Creation of reports containing data and genome annotation

Address of the bookmark: http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/