BOL: Related items

SAMHAR-COVID19 Hackathon

BioStar — Fri, 17 Apr 2020 06:47:10 -0500

Centre for Development of Advanced Computing (C-DAC) under the aegis of the National Supercomputing Mission (NSM), a Ministry of Electronics & Information Technology (MeitY) and Department of Science & Technology (DST) initiative, in association with NVIDIA & OpenACC, announces the SAMHAR-COVID19 Hackathon.

Pandemic outbreak such as Coronavirus outbreak can create huge challenges for the Government and Public Health Officials to gather information quickly and coordinate a response. In such a situation, Artificial Intelligence (AI) can play a huge role in predicting, minimizing and stalling its spread of the virus.

C-DAC has embarked on a program SAMHAR-COVID19 (Supercomputing using AI, ML, Healthcare Analytics based Research for combating COVID19). This opportunity will provide researchers to find solutions for Identifying, Tracking and Forecasting outbreaks of COVID19 and Facilitating Drug Discovery as well.

Participants can update submissions multiple times till the Registration End date.
Each entry can be submitted by a Team comprising of minimum 3 and maximum 5 members (Including the Team Lead).
Participants will have to share the complete work activities with C-DAC. And C-DAC will have right to use the submitted application/solution for SAMHAR-COVID19 programs.
The Award will be given to the Selected/Winning Entry irrespective of the number of members in the Team (members may choose to distribute the amount among themselves).
The decision of the Eminent Jury on the I3 Award will be final and binding.
Award can be for the Team/Company/Institution, as submitted in the Application and cannot be changed later.
Submissions will be considered void if they are in whole or part ill-eligible, incomplete, damaged, altered, counterfeit, obtained through fraud or late submission.

More at https://samhar-covid19hackathon.cdac.in/

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

Rahul Nayak — Mon, 13 Nov 2017 05:10:23 -0600

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

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

MinION_GC: An R script to do some QC on MinION data

Radha Agarkar — Sun, 03 Dec 2017 15:19:18 -0600

Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

MIX: Combining multiple assemblies from NGS data

Rahul Nayak — Tue, 08 May 2018 04:58:05 -0500

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data

Jit — Mon, 06 Aug 2018 17:24:53 -0500

NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.

Address of the bookmark: https://github.com/Nextomics/NextSV

BamView: a free interactive display of read alignments in BAM data files

Neel — Fri, 09 Nov 2018 13:43:22 -0600

To run the application on UNIX from the downloaded jar file run the UNIX:

java -mx512m -jar BamView.jar

and extra command line options are given when '-h' is used:

java -jar BamView.jar -h

BAM files can be specified on the command line with the '-a' option:

java -mx512m -jar BamView.jar -a pathToFile/sorted.bam

If a BAM filename is not given on the command line BamView will prompt for a file to be entered. The BAM index file should have the same name as the BAM file but with a '.bai' suffix. Multiple BAM files can be loaded and overlaid in the viewer. To make this easier BamView will read in files that contain a list of filenames.

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

ZENBU: a collaborative, omics data integration and interactive visualization system

BioJoker — Fri, 04 Jan 2019 13:35:26 -0600

ZENBU is a data integration, data analysis, and visualization system enhanced for RNAseq, ChipSeq, CAGE and other types of next-generation-sequence-tag (NGS) based data. ZENBU allows for novel data exploration through "on-demand" data processing and interactive linked-visualizations and is able to make many-views from the same primary sequence alignment data which users can uploaded from BAM, BED, GFF and tab-text files.
Please check our documentation wiki for details on how to use the system, or check out some of the views above.

Address of the bookmark: http://fantom.gsc.riken.jp/zenbu/

OMTools: a software package for visualizing and processing optical mapping data

BioJoker — Fri, 29 Mar 2019 01:21:54 -0500

OMTools, an efficient and intuitive data processing and visualization suite to handle and explore large-scale optical mapping profiles. OMTools includes modules for visualization (OMView), data processing and simulation. These modules together form an accessible and convenient pipeline for optical mapping analyses.

https://github.com/TF-Chan-Lab/OMTools

Address of the bookmark: https://github.com/TF-Chan-Lab/OMTools

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

pbmm2:A minimap2 frontend for PacBio native data formats

BioStar — Tue, 18 Feb 2020 03:36:22 -0600

pbmm2 is a SMRT C++ wrapper for minimap2's C API. Its purpose is to support native PacBio in- and output, provide sets of recommended parameters, generate sorted output on-the-fly, and postprocess alignments. Sorted output can be used directly for polishing using GenomicConsensus, if BAM has been used as input to pbmm2. Benchmarks show that pbmm2 outperforms BLASR in sequence identity, number of mapped bases, and especially runtime. pbmm2 is the official replacement for BLASR.

Address of the bookmark: https://github.com/PacificBiosciences/pbmm2