神奇青蛙在搬砖

@Magic-Frog

一只青蛙、杀死了BUG、自此走上了搬砖的道路,以后江湖多了一个神奇青蛙的传说......

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关注的仓库(28)

    Watch 神奇青蛙在搬砖/hpc forked from openEuler/hpc
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    openEuler High Performance Computing(HPC) SIG

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/flask-test

    flask项目测试

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/bamtools forked from src-oepkgs/bamtools

    C++ API repodesc command-line toolkit for working with BAM data

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/fsl forked from src-oepkgs/fsl

    A comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/nemo forked from src-oepkgs/nemo

    NEMO: Nucleus for European Modelling of the Ocean is a state-of-the-art modelling framework for research activities and forecasting services in ocean and climate sciences

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/opencv forked from src-oepkgs/opencv

    OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library that includes several hundreds of computer vision algorithms.

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/nanopack forked from src-oepkgs/nanopack

    A set of tools developed for visualization and processing of long-read sequencing data from Oxford Nanopore Technologies and Pacific Biosciences.

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/figtree forked from src-oepkgs/figtree

    FigTree is designed as a graphical viewer of phylogenetic trees and as a program for producing publication-ready figures.

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/deeptools forked from src-oepkgs/deeptools

    deepTools: tools for exploring deep sequencing data.

    最近更新: 3年多前

    Watch 神奇青蛙在搬砖/manta forked from src-oepkgs/manta

    Manta calls structural variants (SVs) and indels from mapped paired-end sequencing reads. It is optimized for analysis of germline variation in small sets of individuals and somatic variation in tumor/normal sample pairs. Manta discovers, assembles and scores large-scale SVs, medium-sized indels and large insertions within a single efficient workflow. The method is designed for rapid analysis on standard compute hardware: NA12878 at 50x genomic coverage is analyzed in less than 20 minutes on a 20 core server, and most WGS tumor/normal analyses can be completed within 2 hours. Manta combines paired and split-read evidence during SV discovery and scoring to improve accuracy, but does not require split-reads or successful breakpoint assemblies to report a variant in cases where there is strong evidence otherwise. It provides scoring models for germline variants in small sets of diploid samples and somatic variants in matched tumor/normal sample pairs. There is experimental support for analysis of unmatched tumor samples as well (see details below). Manta accepts input read mappings from BAM or CRAM files and reports all SV and indel inferences in VCF 4.1 format.

    最近更新: 3年多前

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