Monografie

CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems

Language: Englisch

Identifier: 1134881975

Subject

Baumart; CRISPR/Cas-Methode; Bakterien; Daten

Contributor

Saunders, Sita Johanna
Alkhnbashi, Omer S.
Rose, Dominic
Will, Sebastian
Backofen, Rolf
Albert-Ludwigs-Universität Freiburg / Professur für Bioinformatik
Zentrum für Biosystemanalyse
Albert-Ludwigs-Universität Freiburg / Centre for Biological Signalling Studies
Albert-Ludwigs-Universität Freiburg / Fakultät für Angewandte Wissenschaften
Albert-Ludwigs-Universität Freiburg / Fakultät für Biologie
Albert-Ludwigs-Universität Freiburg

DOI: 10.1093/nar/gkt606

URN: http://nbn-resolving.org/urn:nbn:de:bsz:25-freidok-123476

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 26.01.2023, 1:57 PM CET

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Deutsche Nationalbibliothek

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Object type

Monografie

Associated

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Atom mapping with constraint programming

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Structural constraints and enzymatic promiscuity in the Cas6-dependent generation of crRNAs

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CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

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CRISPRmap: an automated classification of repeat conservation in prokaryotic adaptive immune systems

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Associated

Other Objects (12)

CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci

Atom mapping with constraint programming

Structural constraints and enzymatic promiscuity in the Cas6-dependent generation of crRNAs

CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria

Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data

Analysis of high-throughput sequencing data related to small non-coding RNAs biogenesis and function

Approaches to analysis of chromosome conformation capture data

MICA: multiple interval-based curve alignment

IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data

GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge

CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci

Atom mapping with constraint programming

Structural constraints and enzymatic promiscuity in the Cas6-dependent generation of crRNAs

CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria

Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data

Analysis of high-throughput sequencing data related to small non-coding RNAs biogenesis and function

Approaches to analysis of chromosome conformation capture data

MICA: multiple interval-based curve alignment

IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data

GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge

CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci

Atom mapping with constraint programming

Structural constraints and enzymatic promiscuity in the Cas6-dependent generation of crRNAs

CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria

Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data

Analysis of high-throughput sequencing data related to small non-coding RNAs biogenesis and function

Approaches to analysis of chromosome conformation capture data

MICA: multiple interval-based curve alignment

IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data

GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge

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