Tools for in Silico Discovery of SNPs, SNPpipeline, PolyPhred, PolyBayes Auto SNP and SEAN

Scholarships Dictionary Visa Details Study Abroad Exam Results Online Test Jobs

	Home
Bioinformatics and Data Mining - In Silico Biology Bioinformatics and Data Mining - In Silico Biology - Introduction History of Bioinformatics List of Major Events in Bioinformatics Web Services as Bioinformatics Tools Alignment Tools Some Important Web Sites Commonly Used For Bioinformatics The FASTA Family PatMatch Blast - Earlier Versions Blast - Later Versions ParAlign ProteinEngine and Transeq Interpro Multiple Sequence Alignment Tools Clustalw Phylip BioNumeric Version Multipurpose Analysis Tool / Biotechnix 3D Designing of PCR Primers Tools for In Silico Discovery of SNPs Tools for In Silico Mining of SSRs Assembly Packages for Large Scale Genome Sequencing

Home >> Biotechnology and Genomics >> Bioinformatics and Data Mining - In Silico Biology >> Tools for in Silico Discovery of SNPs

Your Ad Here

Tools for in silico discovery of SNPs

Several programmes that are available for SNP discovery include SNP pipeline, PolyPhred, PolyBayes Auto SNP and SEAN. It has been pointed out that the analytital core of SNPpipeline system is made up of three software components, PHRED, PHRAP and DEMIGLACE. The functions of PolyPhred are also integrated with the use of three other programmes including PHRED, PHRAP and CONSED.

	Home
Demiglace EST Clustering Packages Gene Finders Central Repository Biological Literature / PubMed Bioinformatics Instiutes and Databases / Databanks Bioinformatics Institutes - NCBI, EBI, SIB National Centre for Biotechnology Information - NCBI European Bioinformatics Institute EBI Bioinformatics Center kyoto University Swiss Institute of Bioinformatics - SIB The Databases / Data Banks Swiss-Prot, Prosite and ExPAsy GenBank, Flybase, DDBJ RefSeq Bioinformatics Training at ICGEB Training in Universities Bioinformatics Market Data Mining Selected Companies and Their Areas of Interest in the Field of Bioinformatics Limitations of Bioinformatics and Data Mining