SmORFer: a modular algorithm to detect small ORFs in prokaryotes
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ABSTRACT:
PROVIDER: PRJNA632889 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11791220.fastq.gz | Fastqsanger.gz | |||
| SRR11791221.fastq.gz | Fastqsanger.gz |
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Project description:We developed a novel algorithm, smORFer, detecting smORFs (e.g. <50 codons) which performs with higher accuracy in prokaryotic organisms. smORFer considers structural features of genetic sequence along with in-register translation and using Fourier transform converts them into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way and dependent on the data available different modules can be tested.
2021-05-12 | GSE150601 | GEO
Project description:Emerging evidence places small proteins (≤50 amino acids) more centrally in physiological processes. Yet, their functional identification and the systematic genome annotation of their cognate small open-reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use the 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. They have difficulties evaluating prokaryotic genomes due to the unique architecture (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present a new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting putative smORFs. The unique feature of smORFer is that it uses an integrated approach and considers structural features of the genetic sequence along with in-frame translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way, and dependent on the data available for a particular organism, different modules can be selected for smORF search.
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Project description:Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1,000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385,000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH, and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM’s performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of less than 10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2-5 kb, and 1 kb, respectively. Keywords: CNV detection algorithm development and assessment
2008-03-12 | GSE10511 | GEO
Project description:Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1,000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385,000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH, and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM’s performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of less than 10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2-5 kb, and 1 kb, respectively. Keywords: CNV detection algorithm development and assessment All four samples in this series are hybridizations of genomic DNA from inbred mouse strains 129X1/SvJ versus C57BL6/J. The experiments were performed at increasing resolutions (one 385K, two 2.1M, and one 3.1M).
2008-03-11 | E-GEOD-10511 | biostudies-arrayexpress
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