scholarly journals Improved Search of Large Transcriptomic Sequencing Databases Using Split Sequence Bloom Trees

2016 ◽  
Author(s):  
Brad Solomon ◽  
Carl Kingsford
Keyword(s):  
Population Variation ◽  
Rna Seq ◽  
Specific Expression ◽  
Short Read ◽  
Short Read Sequencing ◽  
Split Sequence ◽  
Transcriptomic Sequencing ◽  
And Storage ◽  
Expressed Sequence ◽  
Over Time

AbstractEnormous databases of short-read RNA-seq sequencing experiments such as the NIH Sequencing Read Archive (SRA) are now available. These databases could answer many questions about the condition-specific expression or population variation, and this resource is only going to grow over time. However, these collections remain difficult to use due to the inability to search for a particular expressed sequence. While some progress has been made on this problem, it is still not feasible to search collections of hundreds of terabytes of short-read sequencing experiments. We introduce an indexing scheme called Split Sequence Bloom Tree (SSBT) to support sequence-based querying of terabyte-scale collections of thousands of short-read sequencing experiments. SSBT is an improvement over the SBT [1] data structure for the same task. We apply SSBT to the problem of finding conditions under which query transcripts are expressed. Our experiments are conducted on a set of 2,652 publicly available RNA-seq experiments contained in the NIH for the breast, blood, and brain tissues. We demonstrate that this SSBT index can be queried for a 1000 nt sequence in under 4 minutes using a single thread and can be stored in just 39 GB, a five-fold improvement in search and storage costs compared to SBT. We further report that SSBT can be further optimized by pre-loading the entire index to accomplish the same search in 30 seconds.

scholarly journals Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees

2015 ◽  
Author(s):  
Brad Solomon ◽  
Carleton Kingsford
Keyword(s):  
Large Scale ◽  
Rna Seq ◽  
Large Collection ◽  
Short Read ◽  
Indexing Scheme ◽  
Short Read Sequencing ◽  
Sequence Read Archive ◽  
Gene Isoform ◽  
Expressed Sequence ◽  
Novel Isoforms

Enormous databases of short-read RNA-seq sequencing experiments such as the NIH Sequence Read Archive (SRA) are now available. However, these collections remain difficult to use due to the inability to search for a particular expressed sequence. A natural question is which of these experiments contain sequences that indicate the expression of a particular sequence such as a gene isoform, lncRNA, or uORF. However, at present this is a computationally demanding question at the scale of these databases. We introduce an indexing scheme, the Sequence Bloom Tree (SBT), to support sequence-based querying of terabase-scale collections of thousands of short-read sequencing experiments. We apply SBT to the problem of finding conditions under which query transcripts are expressed. Our experiments are conducted on a set of 2652 publicly available RNA-seq experiments contained in the NIH for the breast, blood, and brain tissues, comprising 5 terabytes of sequence. SBTs of this size can be queried for a 1000 nt sequence in 19 minutes using less than 300 MB of RAM, over 100 times faster than standard usage of SRA-BLAST and 119 times faster than STAR. SBTs allow for fast identification of experiments with expressed novel isoforms, even if these isoforms were unknown at the time the SBT was built. We also provide some theoretical guidance about appropriate parameter selection in SBT and propose a sampling-based scheme for potentially scaling SBT to even larger collections of files. While SBT can handle any set of reads, we demonstrate the effectiveness of SBT by searching a large collection of blood, brain, and breast RNA-seq files for all 214,293 known human transcripts to identify tissue-specific transcripts. The implementation used in the experiments below is in C++ and is available as open source at http://www.cs.cmu.edu/~ckingsf/software/bloomtree.

scholarly journals RNA-Seq Analysis and Gene Discovery of Andrias davidianus Using Illumina Short Read Sequencing

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123730 ◽  
Author(s):  
Fenggang Li ◽  
Lixin Wang ◽  
Qingjing Lan ◽  
Hui Yang ◽  
Yang Li ◽  
...  
Keyword(s):  
Gene Discovery ◽  
Rna Seq ◽  
Andrias Davidianus ◽  
Short Read ◽  
Short Read Sequencing

scholarly journals Investigation of allele specific expression in various tissues of broiler chickens using the detection tool VADT

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Joseph Tomlinson ◽  
Shawn W. Polson ◽  
Jing Qiu ◽  
Juniper A. Lake ◽  
William Lee ◽  
...  
Keyword(s):  
Broiler Chickens ◽  
Nucleotide Polymorphisms ◽  
Rna Seq ◽  
Specific Expression ◽  
Single Nucleotide ◽  
Allele Specific Expression ◽  
Detection Tool ◽  
Commercial Broiler ◽  
Significant Phenomenon ◽  
Allele Specific

AbstractDifferential abundance of allelic transcripts in a diploid organism, commonly referred to as allele specific expression (ASE), is a biologically significant phenomenon and can be examined using single nucleotide polymorphisms (SNPs) from RNA-seq. Quantifying ASE aids in our ability to identify and understand cis-regulatory mechanisms that influence gene expression, and thereby assist in identifying causal mutations. This study examines ASE in breast muscle, abdominal fat, and liver of commercial broiler chickens using variants called from a large sub-set of the samples (n = 68). ASE analysis was performed using a custom software called VCF ASE Detection Tool (VADT), which detects ASE of biallelic SNPs using a binomial test. On average ~ 174,000 SNPs in each tissue passed our filtering criteria and were considered informative, of which ~ 24,000 (~ 14%) showed ASE. Of all ASE SNPs, only 3.7% exhibited ASE in all three tissues, with ~ 83% showing ASE specific to a single tissue. When ASE genes (genes containing ASE SNPs) were compared between tissues, the overlap among all three tissues increased to 20.1%. Our results indicate that ASE genes show tissue-specific enrichment patterns, but all three tissues showed enrichment for pathways involved in translation.

scholarly journals Identification and Expression Analysis of the Genes Involved in the Raffinose Family Oligosaccharides Pathway of Phaseolus vulgaris and Glycine max

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1465
Author(s):  
Ramon de Koning ◽  
Raphaël Kiekens ◽  
Mary Esther Muyoka Toili ◽  
Geert Angenon
Keyword(s):  
Common Bean ◽  
Seed Development ◽  
Expression Analysis ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Families ◽  
Rna Seq ◽  
Raffinose Family Oligosaccharides ◽  
Specific Expression ◽  
Raffinose Synthase

Raffinose family oligosaccharides (RFO) play an important role in plants but are also considered to be antinutritional factors. A profound understanding of the galactinol and RFO biosynthetic gene families and the expression patterns of the individual genes is a prerequisite for the sustainable reduction of the RFO content in the seeds, without compromising normal plant development and functioning. In this paper, an overview of the annotation and genetic structure of all galactinol- and RFO biosynthesis genes is given for soybean and common bean. In common bean, three galactinol synthase genes, two raffinose synthase genes and one stachyose synthase gene were identified for the first time. To discover the expression patterns of these genes in different tissues, two expression atlases have been created through re-analysis of publicly available RNA-seq data. De novo expression analysis through an RNA-seq study during seed development of three varieties of common bean gave more insight into the expression patterns of these genes during the seed development. The results of the expression analysis suggest that different classes of galactinol- and RFO synthase genes have tissue-specific expression patterns in soybean and common bean. With the obtained knowledge, important galactinol- and RFO synthase genes that specifically play a key role in the accumulation of RFOs in the seeds are identified. These candidate genes may play a pivotal role in reducing the RFO content in the seeds of important legumes which could improve the nutritional quality of these beans and would solve the discomforts associated with their consumption.

scholarly journals Replicate sequencing libraries are important for quantification of allelic imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Asia Mendelevich ◽  
Svetlana Vinogradova ◽  
Saumya Gupta ◽  
Andrey A. Mironov ◽  
Shamil R. Sunyaev ◽  
...  
Keyword(s):  
Allelic Imbalance ◽  
False Positive Rate ◽  
Error Rates ◽  
Differential Analysis ◽  
Rna Seq ◽  
Specific Expression ◽  
Technical Noise ◽  
Specific Analysis ◽  
Positive Rate ◽  
Allele Specific

AbstractA sensitive approach to quantitative analysis of transcriptional regulation in diploid organisms is analysis of allelic imbalance (AI) in RNA sequencing (RNA-seq) data. A near-universal practice in such studies is to prepare and sequence only one library per RNA sample. We present theoretical and experimental evidence that data from a single RNA-seq library is insufficient for reliable quantification of the contribution of technical noise to the observed AI signal; consequently, reliance on one-replicate experimental design can lead to unaccounted-for variation in error rates in allele-specific analysis. We develop a computational approach, Qllelic, that accurately accounts for technical noise by making use of replicate RNA-seq libraries. Testing on new and existing datasets shows that application of Qllelic greatly decreases false positive rate in allele-specific analysis while conserving appropriate signal, and thus greatly improves reproducibility of AI estimates. We explore sources of technical overdispersion in observed AI signal and conclude by discussing design of RNA-seq studies addressing two biologically important questions: quantification of transcriptome-wide AI in one sample, and differential analysis of allele-specific expression between samples.

scholarly journals Biosynthetic potential of uncultured Antarctic soil bacteria revealed through long-read metagenomic sequencing

2021 ◽  
Author(s):  
Valentin Waschulin ◽  
Chiara Borsetto ◽  
Robert James ◽  
Kevin K. Newsham ◽  
Stefano Donadio ◽  
...  
Keyword(s):  
Genome Mining ◽  
Gene Clusters ◽  
Biosynthetic Gene Cluster ◽  
Full Length ◽  
Metagenomic Sequencing ◽  
Short Read ◽  
Short Read Sequencing ◽  
Rich Diversity ◽  
Long Read ◽  
The Rich

AbstractThe growing problem of antibiotic resistance has led to the exploration of uncultured bacteria as potential sources of new antimicrobials. PCR amplicon analyses and short-read sequencing studies of samples from different environments have reported evidence of high biosynthetic gene cluster (BGC) diversity in metagenomes, indicating their potential for producing novel and useful compounds. However, recovering full-length BGC sequences from uncultivated bacteria remains a challenge due to the technological restraints of short-read sequencing, thus making assessment of BGC diversity difficult. Here, long-read sequencing and genome mining were used to recover >1400 mostly full-length BGCs that demonstrate the rich diversity of BGCs from uncultivated lineages present in soil from Mars Oasis, Antarctica. A large number of highly divergent BGCs were not only found in the phyla Acidobacteriota, Verrucomicrobiota and Gemmatimonadota but also in the actinobacterial classes Acidimicrobiia and Thermoleophilia and the gammaproteobacterial order UBA7966. The latter furthermore contained a potential novel family of RiPPs. Our findings underline the biosynthetic potential of underexplored phyla as well as unexplored lineages within seemingly well-studied producer phyla. They also showcase long-read metagenomic sequencing as a promising way to access the untapped genetic reservoir of specialised metabolite gene clusters of the uncultured majority of microbes.

scholarly journals NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

iScience ◽  
2021 ◽  
pp. 102361
Author(s):  
Eliah G. Overbey ◽  
Amanda M. Saravia-Butler ◽  
Zhe Zhang ◽  
Komal S. Rathi ◽  
Homer Fogle ◽  
...  
Keyword(s):  
Rna Seq ◽  
Short Read

scholarly journals REscan: inferring repeat expansions and structural variation in paired-end short read sequencing data

2020 ◽  
Author(s):  
Russell Lewis McLaughlin
Keyword(s):  
Structural Variation ◽  
Sequence Data ◽  
Neurological Diseases ◽  
Repeat Expansion ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Repeat Expansions ◽  
Paired End Sequencing

Abstract Motivation Repeat expansions are an important class of genetic variation in neurological diseases. However, the identification of novel repeat expansions using conventional sequencing methods is a challenge due to their typical lengths relative to short sequence reads and difficulty in producing accurate and unique alignments for repetitive sequence. However, this latter property can be harnessed in paired-end sequencing data to infer the possible locations of repeat expansions and other structural variation. Results This article presents REscan, a command-line utility that infers repeat expansion loci from paired-end short read sequencing data by reporting the proportion of reads orientated towards a locus that do not have an adequately mapped mate. A high REscan statistic relative to a population of data suggests a repeat expansion locus for experimental follow-up. This approach is validated using genome sequence data for 259 cases of amyotrophic lateral sclerosis, of which 24 are positive for a large repeat expansion in C9orf72, showing that REscan statistics readily discriminate repeat expansion carriers from non-carriers. Availabilityand implementation C source code at https://github.com/rlmcl/rescan (GNU General Public Licence v3).

scholarly journals Analysis of Allele-Specific Expression in Mouse Liver by RNA-Seq: A Comparison With Cis-eQTL Identified Using Genetic Linkage

Genetics ◽  
2013 ◽  
Vol 195 (3) ◽  
pp. 1157-1166 ◽  
Author(s):  
Sandrine Lagarrigue ◽  
Lisa Martin ◽  
Farhad Hormozdiari ◽  
Pierre-François Roux ◽  
Calvin Pan ◽  
...  
Keyword(s):  
Mouse Liver ◽  
Genetic Linkage ◽  
Rna Seq ◽  
Specific Expression ◽  
Allele Specific Expression ◽  
Allele Specific
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