scholarly journals Leveraging transcript quantification for fast computation of alternative splicing profiles

2014 ◽  
Author(s):  
Gael P Alamancos ◽  
Amadís Pagès ◽  
Juan L Trincado ◽  
Nicolás Bellora ◽  
Eduardo Eyras
Keyword(s):  
Alternative Splicing ◽  
Rna Sequencing ◽  
De Novo ◽  
Computation Time ◽  
Sequencing Data ◽  
Standard Methods ◽  
Transcript Quantification ◽  
Cellular Processes ◽  
Reconstruction Methods ◽  
Transcript Reconstruction

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available datasets represents a major challenge in terms of computation time and storage requirements. We describe SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA sequencing data compared to experimentally validated events. We assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, we show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large datasets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa

AStrap: identification of alternative splicing from transcript sequences without a reference genome

2018 ◽  
Vol 35 (15) ◽  
pp. 2654-2656 ◽  
Author(s):  
Guoli Ji ◽  
Wenbin Ye ◽  
Yaru Su ◽  
Moliang Chen ◽  
Guangzao Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alternative Splicing ◽  
Single Molecule ◽  
Reference Genome ◽  
De Novo ◽  
Supplementary Information ◽  
Model Organisms ◽  
Sequencing Data ◽  
Extensive Evaluation ◽  
Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Quantifying alternative splicing from paired-end RNA-sequencing data

2014 ◽  
Vol 8 (1) ◽  
pp. 309-330 ◽  
Author(s):  
David Rossell ◽  
Camille Stephan-Otto Attolini ◽  
Manuel Kroiss ◽  
Almond Stöcker
Keyword(s):  
Alternative Splicing ◽  
Rna Sequencing ◽  
Sequencing Data

scholarly journals The rate and spectrum of mosaic mutations during embryogenesis revealed by RNA sequencing of 49 tissues

2019 ◽  
Author(s):  
Francesc Muyas ◽  
Luis Zapata ◽  
Roderic Guigó ◽  
Stephan Ossowski
Keyword(s):  
Rna Sequencing ◽  
De Novo ◽  
Genetic Disorders ◽  
Adult Life ◽  
Diagnostic Procedures ◽  
Cancer Predisposition ◽  
Sequencing Data ◽  
Individual Study ◽  
Similar Frequency ◽  
Mutational Spectrum

AbstractBackgroundMosaic mutations acquired during early embryogenesis can lead to severe early-onset genetic disorders and cancer predisposition, but are often undetectable in blood samples. The rate and mutational spectrum of embryonic mosaic mutations (EMMs) have only been studied in few tissues and their contribution to genetic disorders is unknown. Therefore, we investigated how frequent mosaic mutations occur during embryogenesis across all germ layers and tissues.ResultsUsing RNA sequencing data from the Genotype-Tissue Expression (GTEx) cohort comprising 49 normal tissues and 570 individuals, we found that new-borns on average harbour 0.5 - 1 EMMs in the exome affecting multiple organs (1.3230 × 10−8 per nucleotide per individual), a similar frequency as reported for germline de novo mutations. Our multi-tissue, multi-individual study design allowed us to distinguish mosaic mutations acquired during different stages of embryogenesis and adult life, as well as to provide insights into the rate and spectrum of mosaic mutations. We observed that EMMs are dominated by a mutational signature associated with spontaneous deamination of methylated cytosines and the number of cell divisions. After birth, cells continue to accumulate somatic mutations, which can lead to the development of cancer. Investigation of the mutational spectrum of the gastrointestinal tract revealed a mutational pattern associated with the food-borne carcinogen aflatoxin, a signature that has so far only been reported in liver cancer.ConclusionIn summary, our multi-tissue, multi-individual study reveals a surprisingly high number of embryonic mosaic mutations in coding regions, implying novel hypotheses and diagnostic procedures for investigating genetic causes of disease and cancer predisposition.

scholarly journals YerA41, a Yersinia ruckeri Bacteriophage: Determination of a Non-Sequencable DNA Bacteriophage Genome via RNA-Sequencing

Viruses ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 620
Author(s):  
Katarzyna Leskinen ◽  
Maria I. Pajunen ◽  
Miguel Vincente Gomez-Raya Vilanova ◽  
Saija Kiljunen ◽  
Andrew Nelson ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Transcriptional Control ◽  
De Novo ◽  
Genomic Sequence ◽  
Pcr Amplification ◽  
Yersinia Ruckeri ◽  
Sequencing Data ◽  
Bacterial Gene ◽  
Sequencing Technologies ◽  
Bacterial Gene Expression

YerA41 is a Myoviridae bacteriophage that was originally isolated due its ability to infect Yersinia ruckeri bacteria, the causative agent of enteric redmouth disease of salmonid fish. Several attempts to determine its genomic DNA sequence using traditional and next generation sequencing technologies failed, indicating that the phage genome is modified in such a way that it is an unsuitable template for PCR amplification and for conventional sequencing. To determine the YerA41 genome sequence, we performed RNA-sequencing from phage-infected Y. ruckeri cells at different time points post-infection. The host-genome specific reads were subtracted and de novo assembly was performed on the remaining unaligned reads. This resulted in nine phage-specific scaffolds with a total length of 143 kb that shared only low level and scattered identity to known sequences deposited in DNA databases. Annotation of the sequences revealed 201 predicted genes, most of which found no homologs in the databases. Proteome studies identified altogether 63 phage particle-associated proteins. The RNA-sequencing data were used to characterize the transcriptional control of YerA41 and to investigate its impact on the bacterial gene expression. Overall, our results indicate that RNA-sequencing can be successfully used to obtain the genomic sequence of non-sequencable phages, providing simultaneous information about the phage–host interactions during the process of infection.

scholarly journals Alternative splicing QTLs in European and African populations using Altrans, a novel method for splice junction quantification

2015 ◽  
Author(s):  
Halit Ongen ◽  
Emmanouil T Dermitzakis
Keyword(s):  
Alternative Splicing ◽  
Rna Sequencing ◽  
Active Regions ◽  
Splice Junction ◽  
Population Based ◽  
Transcript Quantification ◽  
Novel Approach ◽  
African Populations ◽  
Different Types ◽  
Novel Method

With the advent of RNA-sequencing technology we now have the power to detect different types of alternative splicing and how DNA variation affects splicing. However, given the short read lengths used in most population based RNA-sequencing experiments, quantifying transcripts accurately remains a challenge. Here we present a novel method, Altrans, for discovery of alternative splicing quantitative trait loci (asQTLs). To assess the performance of Altrans we compared it to Cufflinks, a well-established transcript quantification method. Simulations show that in the presence of transcripts absent from the annotation, Altrans performs better in quantifications than Cufflinks. We have applied Altrans and Cufflinks to the Geuvadis dataset, which comprises samples from European and African populations, and discovered (FDR = 1%) 1806 and 243 asQTLs with Altrans, and 1596 and 288 asQTLs with Cufflinks for Europeans and Africans, respectively. Although Cufflinks results replicated better across the two populations, this likely due to the increased sensitivity of Altrans in detecting harder to detect associations. We show that, by discovering a set of asQTLs in a smaller subset of European samples and replicating these in the remaining larger subset of Europeans, both methods achieve similar replication levels (94% and 98% replication in Altrans and Cufflinks, respectively). We find that method specific asQTLs are largely due to different types of alternative splicing events detected by each method. We overlapped the asQTLs with biochemically active regions of the genome and observed significant enrichments for many functional marks and variants in splicing regions, highlighting the biological relevance of the asQTLs identified. All together, we present a novel approach for discovering asQTLs that is a more direct assessment of splicing compared to other methods and is complementary to other transcript quantification methods.

scholarly journals Fusion detection and quantification by pseudoalignment

2017 ◽  
Author(s):  
Páll Melsted ◽  
Shannon Hateley ◽  
Isaac Charles Joseph ◽  
Harold Pimentel ◽  
Nicolas Bray ◽  
...  
Keyword(s):  
De Novo ◽  
Chromosomal Rearrangements ◽  
Clinical Use ◽  
Gene Fusions ◽  
Fusion Genes ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcript Quantification ◽  
Novel Approach ◽  
Fusion Detection

RNA sequencing in cancer cells is a powerful technique to detect chromosomal rearrangements, allowing for de novo discovery of actively expressed fusion genes. Here we focus on the problem of detecting gene fusions from raw sequencing data, assembling the reads to define fusion transcripts and their associated breakpoints, and quantifying their abundances. Building on the pseudoalignment idea that simplifies and accelerates transcript quantification, we introduce a novel approach to fusion detection based on inspecting paired reads that cannot be pseudoaligned due to conflicting matches. The method and software, called pizzly, filters false positives, assembles new transcripts from the fusion reads, and reports candidate fusions. With pizzly, fusion detection from raw RNA-Seq reads can be performed in a matter of minutes, making the program suitable for the analysis of large cancer gene expression databases and for clinical use. pizzly is available at https://github.com/pmelsted/pizzly

scholarly journals ClusTrast: a short read de novo transcript isoform assembler guided by clustered contigs

2022 ◽  
Author(s):  
Karl Johan Westrin ◽  
Warren W Kretzschmar ◽  
Olof Emanuelsson
Keyword(s):  
Alternative Splicing ◽  
Reference Genome ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Sequencing Data ◽  
Short Read ◽  
Transcript Isoforms ◽  
Short Reads ◽  
Moderate Reduction ◽  
Eukaryotic Species

Motivation: Transcriptome assembly from RNA sequencing data in species without a reliable reference genome has to be performed de novo, but studies have shown that de novo methods often have inadequate reconstruction ability of transcript isoforms. This impedes the study of alternative splicing, in particular for lowly expressed isoforms. Result: We present the de novo transcript isoform assembler ClusTrast, which clusters a set of guiding contigs by similarity, aligns short reads to the guiding contigs, and assembles each clustered set of short reads individually. We tested ClusTrast on datasets from six eukaryotic species, and showed that ClusTrast reconstructed more expressed known isoforms than any of the other tested de novo assemblers, at a moderate reduction in precision. An appreciable fraction were reconstructed to at least 95% of their length. We suggest that ClusTrast will be useful for studying alternative splicing in the absence of a reference genome. Availability and implementation: The code and usage instructions are available at https://github.com/karljohanw/clustrast.

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