<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/38649?offset=190 https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic Tue, 13 Nov 2018 07:25:38 -0600 https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic <![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]> Required python modules

  • biopython
  • intervaltree
  • numpy
  • scipy
  • tqdm
  • PyYAML

Address of the bookmark: https://github.com/cerebis/sim3C

]]> Jit https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data Tue, 22 Jan 2019 06:26:55 -0600 https://bioinformaticsonline.com/bookmarks/view/38762/katuali-is-a-flexible-consensus-pipeline-implemented-in-snakemake-to-basecall-assemble-and-polish-oxford-nanopore-technologies-sequencing-data <![CDATA[Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies' sequencing data]]>
  • Run a pipeline processing fast5s to a consensus in a single command.
  • Recommended fixed "standard" and "fast" pipelines.
  • Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.
  • Seemless distribution of tasks over local or distributed compute.
  • Highly configurable.
  • Open source (Mozilla Public License 2.0).

    • Documentation can be found at https://nanoporetech.github.io/katuali/.

    Address of the bookmark: https://github.com/nanoporetech/katuali

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40251/mosdepth-fast-bamcram-depth-calculation-for-wgs-exome-or-targeted-sequencing Wed, 13 Nov 2019 22:20:19 -0600 https://bioinformaticsonline.com/bookmarks/view/40251/mosdepth-fast-bamcram-depth-calculation-for-wgs-exome-or-targeted-sequencing <![CDATA[mosdepth: fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing]]> mosdepth can output:

    per-base depth about 2x as fast samtools depth--about 25 minutes of CPU time for a 30X genome.
    mean per-window depth given a window size--as would be used for CNV calling.
    the mean per-region given a BED file of regions.
    a distribution of proportion of bases covered at or above a given threshold for each chromosome and genome-wide.
    quantized output that merges adjacent bases as long as they fall in the same coverage bins e.g. (10-20)
    threshold output to indicate how many bases in each region are covered at the given thresholds.
    A summary of mean depths per chromosome and within specified regions per chromosome.

    Address of the bookmark: https://github.com/brentp/mosdepth

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40701/fastgt-an-alignment-free-method-for-calling-common-snvs-directly-from-raw-sequencing-reads Tue, 28 Jan 2020 03:27:33 -0600 https://bioinformaticsonline.com/bookmarks/view/40701/fastgt-an-alignment-free-method-for-calling-common-snvs-directly-from-raw-sequencing-reads <![CDATA[FastGT: an alignment-free method for calling common SNVs directly from raw sequencing reads]]> FastGT is a program package for whole-genome genotyping of genome variants directly from raw sequencing reads. It is written in C and runs in Linux. FastGT uses a list of variant-specific k-mer pairs that are unique in human genome, counts the frequency of k-mers in sequencing data and predicts the genotype. All this takes less than 1 hour on average low-cost Linux server.

    http://bioinfo.ut.ee/FastGT/

    https://github.com/bioinfo-ut/GenomeTester4/

    Address of the bookmark: http://bioinfo.ut.ee/FastGT/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads Mon, 04 May 2020 02:04:03 -0500 https://bioinformaticsonline.com/bookmarks/view/41599/haslr-a-hybrid-assembler-which-uses-both-second-and-third-generation-sequencing-reads <![CDATA[HASLR: a hybrid assembler which uses both second and third generation sequencing reads]]> HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.

    Address of the bookmark: https://github.com/vpc-ccg/haslr

    ]]>     BioStar     https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data Thu, 11 Feb 2021 21:39:05 -0600 https://bioinformaticsonline.com/bookmarks/view/42826/ktrim-an-extra-fast-and-accurate-adapter-and-quality-trimmer-for-sequencing-data <![CDATA[Ktrim: an extra-fast and accurate adapter- and quality-trimmer for sequencing data]]> 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

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/36026/mmseqs20-ultra-fast-and-sensitive-protein-search-and-clustering-suite Thu, 22 Mar 2018 10:40:51 -0500 https://bioinformaticsonline.com/bookmarks/view/36026/mmseqs20-ultra-fast-and-sensitive-protein-search-and-clustering-suite <![CDATA[MMseqs2.0: ultra fast and sensitive protein search and clustering suite]]> MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

    The MMseqs2 user guide is available as Github Wiki or as PDF file (Thanks to pandoc!)

    Please cite: Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).

    Address of the bookmark: https://github.com/soedinglab/MMseqs2

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/44559/metagraph-ultra-scalable-framework-for-dna-search-alignment-assembly Sat, 08 Jun 2024 16:15:25 -0500 https://bioinformaticsonline.com/bookmarks/view/44559/metagraph-ultra-scalable-framework-for-dna-search-alignment-assembly <![CDATA[MetaGraph: Ultra Scalable Framework for DNA Search, Alignment, Assembly]]> The MetaGraph framework is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle, transforming single input samples into error-removed, refined sample graphs, which are then merged into a joint metagraph index. Each input sample is annotated in the joint index as a subgraph. This graph index enriched with metadata can then be used for downstream applications such as sequence search or differential assembly.

    Searcg link https://metagraph.ethz.ch/search 

    Pre-print https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4 

    Address of the bookmark: https://metagraph.ethz.ch/

    ]]>     Abhi     https://bioinformaticsonline.com/blog/view/40404/exchange-programme-for-indian-scientist Wed, 18 Dec 2019 21:11:22 -0600 https://bioinformaticsonline.com/blog/view/40404/exchange-programme-for-indian-scientist <![CDATA[Exchange Programme for Indian scientist !!]]> The Indian National Science Academy (INSA) is a premier scientific learned body (established in 1935) representing all branches of science –Physical and Biological Sciences including Engineering, Medicine and Agricultural Sciences. The Academy has been promoting scientific cooperation with Academies/Organisations of several countries the world over. The Academy has links with the Academies and Organisations in Asia, Europe
    and South America. These programmes provide opportunities to scientists working in various scientific institutions and organizations in the country for exchange of ideas, knowledge, establish new links, strengthen old links and undertake joint projects with their research partners in leading laboratories and institutions abroad.

    The Academy has an International Exchange Programme with Academies/Organizations in the countries: Brazil, China, France, Hungary, Iran, Israel, Nepal, Philippines, Poland, Scotland, Slovak Republic, Republic of Slovenia, Sudan and Taiwan.

    Applications are invited from Indian Nationals for consideration by the Academy for the next calendar year.

    • The applicant should be a scientist holding a regular (permanent) position in a recognized S & T Institution/University and actively engaged in research work in frontline areas.
    • He/She should not have been abroad during the last 3 years under any INSA Programme.
    • The scientist should have been accepted to work in an Institute/Laboratory in the country to be visited and this should be supported by a letter of invitation from the host abroad.
    • Those who wish to visit abroad for three months should submit a detailed programme of their collaborative research work to be conducted.

    All applications duly completed should be forwarded to the academy through proper channel by the employer/head of the Institute.

    • Scientists selected for deputation abroad would be provided 100% travel support (by only Air India excursion class airfare, through shortest route from the place of duty in India to the nearest airport of host Institute and back) by INSA.
    • Medical Insurance purchased in India.
    • Visa fee (if any).
    • The receiving Academy/Organization would provide local hospitality including internal travel abroad.

    Contact for detail at 

    www.insaindia.res.in

    INDIAN NATIONAL SCIENCE ACADEMY
    Bahadur Shah Zafar Marg, New Delhi – 110 002.
    Telephone: 91-11-23221931 – 23221950 (EPABX),
    Fax: 91-11- 23235648, 23231095

    ]]>     Shruti Paniwala     https://bioinformaticsonline.com/view/119 Wed, 10 Jul 2013 14:35:34 -0500 https://bioinformaticsonline.com/view/119 <![CDATA[Which are the best statistical programming languages to study for a bioinformatician?]]> In Bio-informatics based genome sequencing and predicting metabolic pathways research jobs I used Matlab, SAS, SPSS, R and several Bioconductor packages. Matlab had a lot of powerful tools and was easy to use, whereas SPSS is for non-programmers and R need programming skills. I am wondering what other people think is best? or there might not be one specific language but a few that lend themselves best to Bio-informatics work that is math heavy and deals with a large amount of data.

    ]]>     Jitendra Narayan