![]() We didn't use the reference assembler much. It does show you the paired ends mapped to the result though. It appears it does by the way the GUI presents it, but tech support confirmed it doesn't use it to link contigs. The main issue is that the CLC de novo assembler did (or does still?) not support PAIRED END assembly (unlike Velvet). The results were similar to what Velvet gave (based on some resequencing results). We found the CLC "de novo" assembler to be very slow compared to Velvet. Traditionally we have used Velvet for assembly, Shrimp/MAQ for SNP analysis, and Artemis and in-house applications and scripts for the rest. We also tried it on a mixture of Win32, Win64, Mac OS X and Linu圆4 machines - ranging from single core 2 GB to 8 way 64 GB RAM machines. Our main application area is prokaryotic sequencing and transcript analysis using Illumina GA2, so de novo assembly and SNP reporting was important. CLC was generous with temporary licences throughout the process. The department I work within spent a fair amount of time evaluating it, and recently purchased a few full licences. I hope this was of help and please feel free to post any questions or comments to this that you may have. For this reason we provide a Software Developer Kit which gives access to an extensive and well supported API and a developer community. For this reason, we are focusing on providing an open industry-strength platform that users can modify and extend. However, although we intend to provide a very comprehensive tool set we know that we can not cover all applications there is. Improved detection of genome scale eventsįurther down the line we are looking at including features like:.Advanced feature queries – feature tracks.Having established a firm basis for secondary analysis we have an ambitious roadmap for including more tertiary analysis tools later this year. This package is a separate product which includes the fast assembly algorithms and a number of utilities for handling assembly results. Smoother handling of hybrid data sets (cross-platform, cross-experiment-design)Īlongside Genomics WB 2.0, we are also releasing a command line program package for de novo and reference assembly which will give users access to these tools in a scripting environment.A completely new short read assembler delivering the worlds fastest reference assembly – click here for more info and white paper.This includes the following improvements: Version 2.0 of the software is out in a few days, and for this release we have focused on bringing our Workbench to a state where it can comfortably handle human genome size data sets. However, we have also included some tertiary analyses like SNP detection and graphical identification of large scale genomic events.įor a full feature list, have a look here. The objective of the CLC Genomics Workbench is to create an integrated bioinformatics environment which combines the power to handle the magnitude of NGS data with a carefully designed graphical user interface.įor the first version we have focused on handling the secondary level of NGS bioinformatics, namely de novo assembly and reference assembly. The Genomics Workbench was created to address these challenges. Next generation sequencing technologies are causing some dramatic changes in the high-throughput sequencing landscape and in turn generating a lot of challenges to the field of bioinformatics. $SERVERCMD -A mkdir -t $ -O table_export_result.txtĬheck_return_code "Export variant table to excel"įile=`$PARSECMD -f table_export_result.Several people have requested that we wrote an introduction to the CLC Genomics Workbench, so here goes. Variation 찾는 workflow script 예제 #Ģ) Command Line tools 실행파일 패스 설정 SERVERCMDPATH="clcserver"ģ) import data 디렉토리 설정 (GxServer import data location으로 설정한다.) IMPORTPATH="clc://serverfile/data/CLC_data/import_data"Ĥ) 결과 파일 저장 디렉토리 설정(서버폴더) DATAPATH="clc://server/CLC_data" 참고로 현재 CLC Server Command Line Tools 2.1 버전에서는 약 84가지의 CLC Server Program(Algorithm)을 사용할 수 있다. 아래 예제 스크립트는 CLC Server Programe 중 read_mapping(Map Reads to Reference)와 quality-based_variant_detection(Quality-based Variant Detection) 를 사용하여 Variation 분석하는 간단한 예제 스크립트이다. ![]()
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