BOL: Related items

Your stress/depression came from ancestor

Rahul Agarwal — Sun, 04 May 2014 18:46:20 -0500

"A study published in Nature Neuroscience finds that stress in early life alters the production of small RNAs, called microRNAs, in the sperm of mice. The mice show depressive behaviours that persist in their progeny."

Source:

http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049

GeneProf: analysis of high-throughput sequencing experiment

Jit — Wed, 05 Jul 2017 16:47:05 -0500

GeneProf is a web-based, graphical software suite that allows users to analyse data produced using high-throughput sequencing platforms (RNA-seq and ChIP-seq; "Next-Generation Sequencing" or NGS): Next-gen analysis for next-gen data!

Some of GeneProf's highlights include:

Easy-to-use web-based interface:Access your data at any time from any computer with a working internet connection -- no need to install software! (cp. Section 'System Requirements').
Analysis wizards make your life easy:Step-by-step workflows make it easy to analyse high-throughput data within a minimum of hands-on time. (cp. SubConcept 'Analysis Wizards').
Versatile modules:Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely into sophisticated workflows (cp. Concept 'Workflows').
Integrated Analysis:Analysis of ChIP-seq and RNA-seq data in one place, plus support for the integration of other external data (e.g. from microarrays).
Comprehensive Resource:GeneProf provides a comprehensive resource of fully analyzed next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent (cp. Tutorial 'Examining Public Next-Gen Data..').
Extensibility:Algorithm developers and computer programmers can develop their own modules and extend GeneProf. Existing software can be easily wrapped in the workflow framework (cp. Section 'Module Development: Adding new..') and data from GeneProf may be used externally (cp. Section 'Web API: Retrieving Data from ..').

GeneProf is academic software developed at the Centre for Regenerative Medicine / Institute for Stem Cell Research, University of Edinburgh and has benefited from funding by the Medical Research Council and the EU Framework 7 Project "EuroSyStem".

Address of the bookmark: https://www.geneprof.org/GeneProf/index.jsp

AIRVF: a filtering toolbox for precise variant calling in Ion Torrent sequencing

BioStar — Fri, 22 Dec 2017 00:31:06 -0600

AIRVF that works on flowgram, raw and mapped reads and called variants to reduce artifact-driven false variant calls. Tests on sequencing data of standard reference material showed up to ∼98% reduction of false variants when combined to conventional public pipelines and ∼48% to the in-house commercial solution, with a minimal loss of sensitivity

The program with a detailed manual is available at https://sourceforge.net/projects/airvf/

Address of the bookmark: https://sourceforge.net/projects/airvf/

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

gSearch: a fast and flexible general search tool for whole-genome sequencing

Jit — Mon, 06 Aug 2018 17:19:15 -0500

gSearch compares sequence variants in the Genome Variation Format (GVF) or Variant Call Format (VCF) with a pre-compiled annotation or with variants in other genomes. Its search algorithms are subsequently optimized and implemented in a multi-threaded manner.

Address of the bookmark: http://ml.ssu.ac.kr/gSearch/index.html

d2Tools: The toolbox for counting the frequency of k-tuple from sequencing datasets and calculate the dissimilarity

Jit — Thu, 20 Sep 2018 08:38:29 -0500

d2Tools are the toolbox for counting the frequency of K-tuple from sequencing datasets and then calculating the pairwise dissimilarity matrix between samples with the d2-style(d2/d2*/d2S representing d2/d2Star/d2shepp, respectively) measures. Hao, Dai, Eucliean, Mahattan, and Chebyshev distance measures are also included in d2Tools.

Manual at https://code.google.com/archive/p/d2-tools/wikis/d2ToolMannual.wiki

Address of the bookmark: https://code.google.com/archive/p/d2-tools/

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

Radha Agarkar — Fri, 04 Jan 2019 10:10:48 -0600

EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.

EXCAVATOR is a novel software package for the detection of copy number variants (CNVs) from whole-exome sequencing data.
EXCAVATOR has been published on Genome Biology (http://genomebiology.com/2013/14/10/R120/abstract).

Address of the bookmark: https://sourceforge.net/projects/excavatortool/

Prof. Dr. med. Andreas Ramming

Wed, 07 Aug 2019 03:25:48 -0500

In many autoimmune diseases, a misdirected immune response leads to chronic inflammation and subsequently to fibrotic and degenerative tissue remodeling. Therapeutic options are available for inflammatory joint diseases, but only about 40% of patients respond to these existing therapies on a permanent basis. In the remaining cases, these therapies miss their target from the beginning or later during the course of treatment failure. There are currently no causal therapies available for the treatment of fibrotic autoimmune diseases such as systemic sclerosis. Therefore, there is an urgent need to develop new therapeutic options for the treatment of fibrotic and synovitic autoimmune diseases. His group is therefore deal with the molecular mechanisms of these misdirected signaling pathways for the development of novel, targeted therapies

http://www.medizin3.uk-erlangen.de/forschung/arbeitsgruppen/matrixbiologie-entzuendliche-signalwege-in-arthritis-und-fibrose/

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

Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data

Jit — Thu, 11 Feb 2021 21:39:05 -0600

Ktrim is written in C++ for GNU Linux/Unix platforms. After uncompressing the source package, you can find an executable file ktrim under bin/ directory compiled using g++ v4.8.5 and linked with libz v1.2.7 for Linux x86_64 system. If you could not run it (which is usually caused by low version of libc++ or libz library) or you want to build a version optimized for your system, you can re-compile the programs:

user@linux$ make clean && make

Address of the bookmark: https://github.com/hellosunking/Ktrim