Dataset Information

DeepBlockAlign: a tool for aligning RNA-seq profiles of read block patterns.

ABSTRACT: MOTIVATION: High-throughput sequencing methods allow whole transcriptomes to be sequenced fast and cost-effectively. Short RNA sequencing provides not only quantitative expression data but also an opportunity to identify novel coding and non-coding RNAs. Many long transcripts undergo post-transcriptional processing that generates short RNA sequence fragments. Mapped back to a reference genome, they form distinctive patterns that convey information on both the structure of the parent transcript and the modalities of its processing. The miR-miR* pattern from microRNA precursors is the best-known, but by no means singular, example. RESULTS: deepBlockAlign introduces a two-step approach to align RNA-seq read patterns with the aim of quickly identifying RNAs that share similar processing footprints. Overlapping mapped reads are first merged to blocks and then closely spaced blocks are combined to block groups, each representing a locus of expression. In order to compare block groups, the constituent blocks are first compared using a modified sequence alignment algorithm to determine similarity scores for pairs of blocks. In the second stage, block patterns are compared by means of a modified Sankoff algorithm that takes both block similarities and similarities of pattern of distances within the block groups into account. Hierarchical clustering of block groups clearly separates most miRNA and tRNA, and also identifies about a dozen tRNAs clustering together with miRNA. Most of these putative Dicer-processed tRNAs, including eight cases reported to generate products with miRNA-like features in literature, exhibit read blocks distinguished by precise start position of reads. AVAILABILITY: The program deepBlockAlign is available as source code from http://rth.dk/resources/dba/. CONTACT: gorodkin@rth.dk; studla@bioinf.uni-leipzig.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUBMITTER: Langenberger D

PROVIDER: S-EPMC3244762 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

deepBlockAlign: a tool for aligning RNA-seq profiles of read block patterns.

Langenberger David D Pundhir Sachin S Ekstrøm Claus T CT Stadler Peter F PF Hoffmann Steve S Gorodkin Jan J

Bioinformatics (Oxford, England) 20111103 1

<h4>Motivation</h4>High-throughput sequencing methods allow whole transcriptomes to be sequenced fast and cost-effectively. Short RNA sequencing provides not only quantitative expression data but also an opportunity to identify novel coding and non-coding RNAs. Many long transcripts undergo post-transcriptional processing that generates short RNA sequence fragments. Mapped back to a reference genome, they form distinctive patterns that convey information on both the structure of the parent trans ...[more]

PMID: 22053076

Similar Datasets

Project description:MotivationDeep sequencing of the transcripts of regulatory non-coding RNA generates footprints of post-transcriptional processes. After obtaining sequence reads, the short reads are mapped to a reference genome, and specific mapping patterns can be detected called read mapping profiles, which are distinct from random non-functional degradation patterns. These patterns reflect the maturation processes that lead to the production of shorter RNA sequences. Recent next-generation sequencing studies have revealed not only the typical maturation process of miRNAs but also the various processing mechanisms of small RNAs derived from tRNAs and snoRNAs.ResultsWe developed an algorithm termed SHARAKU to align two read mapping profiles of next-generation sequencing outputs for non-coding RNAs. In contrast with previous work, SHARAKU incorporates the primary and secondary sequence structures into an alignment of read mapping profiles to allow for the detection of common processing patterns. Using a benchmark simulated dataset, SHARAKU exhibited superior performance to previous methods for correctly clustering the read mapping profiles with respect to 5'-end processing and 3'-end processing from degradation patterns and in detecting similar processing patterns in deriving the shorter RNAs. Further, using experimental data of small RNA sequencing for the common marmoset brain, SHARAKU succeeded in identifying the significant clusters of read mapping profiles for similar processing patterns of small derived RNA families expressed in the brain.Availability and implementationThe source code of our program SHARAKU is available at http://www.dna.bio.keio.ac.jp/sharaku/, and the simulated dataset used in this work is available at the same link. Accession code: The sequence data from the whole RNA transcripts in the hippocampus of the left brain used in this work is available from the DNA DataBank of Japan (DDBJ) Sequence Read Archive (DRA) under the accession number DRA004502.Contactyasu@bio.keio.ac.jpSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:Single-cell RNA sequencing (scRNA-seq) technologies have precipitated the development of bioinformatic tools to reconstruct cell lineage specification and differentiation processes with single-cell precision. However, current start-up costs and recommended data volumes for statistical analysis remain prohibitively expensive, preventing scRNA-seq technologies from becoming mainstream. Here, we introduce single-cell amalgamation by latent semantic analysis (SALSA), a versatile workflow that combines measurement reliability metrics with latent variable extraction to infer robust expression profiles from ultra-sparse sc-RNAseq data. SALSA uses a matrix focusing approach that starts by identifying facultative genes with expression levels greater than experimental measurement precision and ends with cell clustering based on a minimal set of Profiler genes, each one a putative biomarker of cluster-specific expression profiles. To benchmark how SALSA performs in experimental settings, we used the publicly available 10X Genomics PBMC 3K dataset, a pre-curated silver standard from human frozen peripheral blood comprising 2,700 single-cell barcodes, and identified 7 major cell groups matching transcriptional profiles of peripheral blood cell types and driven agnostically by < 500 Profiler genes. Finally, we demonstrate successful implementation of SALSA in a replicative scRNA-seq scenario by using previously published DropSeq data from a multi-batch mouse retina experimental design, thereby identifying 10 transcriptionally distinct cell types from > 64,000 single cells across 7 independent biological replicates based on < 630 Profiler genes. With these results, SALSA demonstrates that robust pattern detection from scRNA-seq expression matrices only requires a fraction of the accrued data, suggesting that single-cell sequencing technologies can become affordable and widespread if meant as hypothesis-generation tools to extract large-scale differential expression effects.

Dataset Information

DeepBlockAlign: a tool for aligning RNA-seq profiles of read block patterns.

Publications

deepBlockAlign: a tool for aligning RNA-seq profiles of read block patterns.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets