This site needs JavaScript to work properly. Funding acquisition, Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. Rare variant studies are already routinely performed as whole-exome sequencing studies. 2020 Oct 8;21(1):703. doi: 10.1186/s12864-020-07107-7. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. 2009;10: 57–63. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Metzker ML. The SNP calling step uses the GATK toolkit for splitting “N” cigar reads (i.e. 66% of the coding variants identified in WGS data were found in RNA-seq. Table 2 provides the summary of mapping and variant calling statistics from the multiple aligners. A low percentage (10%) of our RNA-seq SNPs overlap with the 600k SNPs (Fig 9), which is largely due to the limitation in the number of variants the genotyping panel is able to capture across different samples. here. Variant calling was performed using Picard and GATK HaplotypeCaller, following the recommendations proposed by Van der Auwera et al [24] and Yiyuan Yan et al [25]. 2017;18: 690 10.1186/s12864-017-4022-x S1A). therefore increasingly require scalable variant analysis solutions. here. Comparison of RNA-seq SNPs identified in the different mapping tools. Despite the limitations of calling genomic variants from RNA-seq data, our work shows high sensitivity and specificity in SNP calls from RNA-seq data. The practical sessions will focus on running the GATK pipeline from the Broad institute. Over 65% of WGS coding variants were identified from RNA-seq. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. FastQ files are QC using FastQC, mapped using three aligners. The final results were exported, including a raw VCF of all the genotype calls and a txt file of all variants with > = 97% call rate. Roles Author: Vince Forgetta. No, Is the Subject Area "Transcriptome analysis" applicable to this article? Overall the results indicate that RNA-seq can be an accurate method of SNP detection using our VAP workflow. Click through the PLOS taxonomy to find articles in your field. A high proportion of SNPs detected in RNA-seq data are true variants. The variant sites showed a clear enrichment of transitions, inclusive of A>G and T>C mutations (73.9%), indicative of mRNA editing and the dominant A-to-I RNA editing [28] (Fig 4). After filtering, 282,798 (54.9%) high confidence SNPs remain, of which 97.2% (274,777 SNPs) were supported by evidence from WGS or dbSNP v.150 (Fig 3). The value of this contribution would greatly increase if the pipeline consolidated the output of the different tools. Adetunji MO(1), Lamont SJ(2), Abasht B(1), Schmidt CJ(1). Fig 2. We implemented an analysis pipeline that detects genetic variants and annotates each variant with the key information needed by the geneticist. Most of the predicted SNPs were homozygous to the non-reference allele, confirming high level of inbreeding in Fayoumi [29,30]. To conduct rare variant analysis on a genome wide scale using programs such as VT, SKAT, and RR. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. No, Is the Subject Area "Gene expression" applicable to this article? Due to difficulty in annotating and determining the impact of polymorphisms on non-coding or regulatory regions, only polymorphisms found on coding regions were further evaluated. in chicken embryos [28] (Table 5). 66% of the coding variants identified in WGS data were found in RNA-seq. Simplify rare variant analysis and interpretation by calling, prioritizing, and reporting on variants from one software interface. The precision of the VAP workflow was determined as the number of all known RNA-seq variants divided by the total number of known and novel RNA-seq variants, i.e. It includes four existing tools. Our study demonstrates that variants calling from RNA-seq experiments can tremendously benefit from an increased number of reads increasing the coverage of genomic regions especially for whole genome analysis; nevertheless even our small sample size allowed for reliable calling of variants and enriching for variants in exonic regions. broad scope, and wide readership – a perfect fit for your research every time. The priority SNPs were filtered using the GATK Variant Filtration tool and custom Perl scripts. Approximately 66% of the coding variants identified by WGS were discovered using RNA-seq alone (Fig 6). Similar filtering parameters for RNA-seq as previously described were applied using the GATK Variant Filtration tool and custom scripts (Table 1). Detection of single nucleotide polymorphisms (SNPs) is an important step in understanding the relationship between genotype and phenotype. SNPs found in WGS data or present in dbSNP (Build 150) are identified as “verified” variants, while those not found are tagged as “novel”. Fig 7. Most methods for variant identification utilize whole-genome or whole-exome sequencing data, while variant identification using RNA-seq remains a challenge because of the complexity in the transcriptome and the high false positive rates [2]. Comparison of RNA-seq SNPs identified…. Once SNPs have been identified, SnpEff is utilized to annotate and predict the effects of the variants. SAMtools was used to convert the alignment results to BAM format [16]. FastQ files are QC using FastQC, mapped using three aligners. We mapped the WGS data with BWA-mem (v 0.7.16a-r1181) [23] using default parameters to the NCBI Gallus gallus Build 5.0 reference genome. 2013;93: 641–651. Our mini-pipeline will download HapMap data, sub-sample at 1% and 10%, do a simple PCA, and draw it. RNA-seq from different tissues) can increase the coverage thereby facilitate variant discovery in regions of interest that would have otherwise been missed. Not surprisingly, the majority of the 600K genotyping variants were also identified in dbSNP, proving that dbSNP an excellent choice for in silico validation. Variants located in the MHC region (6:28,510,120–33,480,577 GRCh38) are excluded from the fine-mapping pipeline. Whole-exome sequencing data analysis pipeline ... For this, we’ll use Variant Calling application based on samtools mpileup: The app automatically scans every position along the genome, computes all the possible genotypes from the aligned reads, and calculates the probability that each of these genotypes is truly present in your sample. Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data. Autoři: Modupeore O. Adetunji aff001; Susan J. Lamont aff002; Behnam Abasht aff001; Carl J. Schmidt aff001 Působiště autorů: Department of Animal and Food Sciences, Universit While specificity is estimated as the number of TS divided by the number of TS plus the number of DS (i.e. 2020 Oct 6;21(19):7386. doi: 10.3390/ijms21197386. The mutational profile of RNA-seq…. The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. eSNV-detect [6] relies on combination of two aligners (BWA and TopHat2) followed by variant calling with SAMtools. Requirements. Overall, we present a valuable methodology that provides an avenue to analyze genomic SNPs from RNA-seq data alone. Lastly, the filtering steps entail assigning priority to SNPs found in all three mapping plus SNP calling steps, to minimize false positive variant calls. We propose that calculating specificity will estimate the likelihood of detecting a true variant in RNA-seq and sensitivity will determine how likely RNA-seq is able to detect an expressed SNP if it is present in a transcribed gene [9]. The discrepancy among single nucleotide variants detected by DNA and RNA high throughput sequencing data. Fig 4. RNA editing is the most prevalent form of post-transcriptional maturation processes that contributes to transcriptome diversity. Int J Mol Sci. 2020 Aug 3;20(1):365. doi: 10.1186/s12870-020-02564-4. Three pipelines, namely GenomeAnalysisToolKit (version 4.0.5.2) (McKenna et al., 2010; Francioli et al., 2017), RTG (non-commercial version 3.9.1) (Cleary et al., 2014) and VarScan (version 2.3.9) (Koboldt et al., 2013), were applied in this study to call the DNSNVs. The pipeline will be effective as of June 1 st 2019 and will become our new standard for genome analyses, including low-frequency variant detection. From our dataset, we identified the three non-synonymous RDD mutations on CYFIP2, GRIA2 and COG3 previously validated by Frésand et al. R libraries: VT and its dependencies: Rsge, getopt, doMC; SKAT and its dependencies. Writing – review & editing, Affiliation Writing – original draft, Support for Variant Analysis Personal Genome Pipeline (i.e., “Single sample”) has been removed from the “Ingenuity Variant Analysis… https://doi.org/10.1371/journal.pone.0216838.t003. Heads up! Lam S, Zeidan J, Miglior F, Suárez-Vega A, Gómez-Redondo I, Fonseca PAS, Guan LL, Waters S, Cánovas A. BMC Genomics. SNP genotyping offers a highly accurate and alternative method of SNP discovery, and thus offers an additional in silico method of validation of our RNA-seq SNPs. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. We then compared the RNA-seq SNPs in expressed genes (having FPKM > 0.1), and the specificity increased from 66% to over 82% (Fig 7). All micro-array data are available from the Gene Expression Omnibus database (accession number GSE131764). The compatibility between input read regions, variants, and reference sequence is checked more consistently in Ingenuity Variant Analysis tools and workflows. https://doi.org/10.1371/journal.pone.0216838.t004. Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data Adetunji MO, Lamont SJ, Abasht B, Schmidt CJ (2019) Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data. Having matched RNA and DNA samples allows for suitable verification of RNA SNP calls, making our datasets good candidates for evaluating the accuracy of our VAP methodology. Department of Animal and Food Sciences, University of Delaware, Newark, Delaware, United States of America, Roles Genome-Wide Development and Validation of Cost-Effective KASP Marker Assays for Genetic Dissection of Heat Stress Tolerance in Maize. (a) all autosomal SNPs and (b) autosomal SNPs found in exons. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. Also, SNPs not detected in RNA-seq but found in WGS and validated using dbSNP are called “DNA-verified” SNPs (DS). We obtained RNA-seq and whole genome sequencing (WGS) data for highly inbred Fayoumi chickens from previously published works. ∙ 0 ∙ share . Resources, Several methodologies have provided approaches to understanding the varied aspects occurring in the transcriptome, but little has been done in its application to identifying variants in functional regions of the genome. As mentioned before, our RNA-seq SNPs were notably contributed from transitions which may be attributed to mRNA editing. Here, we will develop a mini variant analysis pipeline with Airflow. Distribution of expression levels for genes with RNA-seq SNPs. Further classifications of the RNA-seq SNPs detected in exons reveal 34% of the exonic SNPs verified by dbSNP were not identified in our WGS data. The use of the splice-aware aligner allows for accurate assembly of reads because it makes use of both the genome and transcriptome information simultaneously for read mapping. The verified sites exhibited a transition-to-transversion (ts/tv) ratio of 2.84 and estimated ts/tv ratio of ~5 for exonic regions and thus a good indicator of genomic conservation in transcribed regions. for variant discovery, is key to the mainstream adoption of High Throughput technology for disease prevention and for clinical use. 234 million for RNA-seq compared to the 482 million for WGS sequencing reads used in our case study). However, a low overlap with the 600K chicken genotyping panel was observed (Fig 9). -, Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. https://doi.org/10.1371/journal.pone.0216838.g002, https://doi.org/10.1371/journal.pone.0216838.g003, https://doi.org/10.1371/journal.pone.0216838.g004. Sensitivity analysis will evaluate the accuracy of our pipeline to correctly detect known SNPs using RNA-seq, and specificity analysis will assess how likely a SNP is detected by RNA-seq compared to WGS. In addition these workflows either rely on outdated variant calling procedures, or do nothing to address the existing bias in the read alignment step towards false positives calls as a result of the transcriptome complexity, thus making it difficult to sufficiently compare their performance. Given that RNA-seq required less sequencing effort and computational requirements (e.g. Both samples were sequenced on the Illumina HiSeq platform. Contact: [email protected] Can anyone here recommend a pipeline for me to basically take my RNA-seq data and either 1) re-align using a newer reference genome or 2) use the existing *.bam files to perform variant analysis to find sequence differences? Distribution of expression levels for…. Data curation, Conceptualization, The authors have declared that no competing interests exist. The txt file was utilized to filter low quality variants from the raw VCF. However, having access to RNA sequences at a single nucleotide resolution provides the opportunity to investigate gene or transcript differences across species at a nucleotide level. -, Guo Y, Zhao S, Sheng Q, Samuels DC, Shyr Y. This low overlap is most likely due to the limitations in genotyping panels currently available for any given organism. To streamline analysis, the user could also set up variant annotation when setting up a de novo Consequently, these RDD sites may result from post-transcriptional modification of the RNA sequence, such as RNA editing or alternative splicing. Yes For the remaining (novel) 8,021 SNPs, we observed slightly lower ts/tv ratio (2.81) than for the verified sites. (a) all autosomal SNPs and (b) autosomal SNPs found in exons. Proteoform Identification by Combining RNA-Seq and Top-Down Mass Spectrometry. 2021 Jan 1;20(1):261-269. doi: 10.1021/acs.jproteome.0c00369. Synopsis. The variant annotation pipeline is fully integrated with Bionano Access™.  |  Fig 8. However, the remaining WGS coding variants were not detected as a result of either: lack of expression/transcription (“no transcription”), the position was homozygous in RNA (“no variation”), “found but filtered” signifying that the position was detected but removed by one of our filtering steps, or “filtered” which indicates the position was heterozygous but filtered because it didn’t meet the default parameters for variant detection. We used ANNOVAR (v 2017Jul16) and VEP (v 91) to annotate variants on the basis of gene model from RefSeq, Ensembl and the UCSC Genome Browser. The authors describe a pilot version of an integrated pipeline of network analysis tools for genomic variants. Specificity = TS / (TS + DS)) [5,9]. Nevertheless, VAP allows the detection of variants even for lowly expressed genes. No, Is the Subject Area "Genomics" applicable to this article? The sensitivity of SNP calls are similar for both heterozygous and homozygous sites (Fig 5). Supervision, We will look at a complete workflow, from data QC to functional interpretation of variant calls. Our results show very high precision, sensitivity and specificity, though limited to SNPs occurring in transcribed regions. Summary statistics were harmonised to ensure that the ALT allele is always the effect allele, and were pre-filtered to remove variants with low minor allele counts which would lead to inaccurate effect estimation. The source code and user manuals are available at https://modupeore.github.io/VAP/. Data Availability: All relevant data are within the paper. Formal analysis, National Center for Biotechnology Information, Unable to load your collection due to an error, Unable to load your delegates due to an error. Sequencing technologies the next generation. SNPs were filtered using the set of read characteristics summarized in Table 1; low quality calls (QD < 5), or variants with strong strand bias (FS > 60), or low read depth (DP < 10) and SNP clusters (3 SNPs in 35bp window) were excluded from further analysis. VAP uses a multi-aligner concept to call SNPs confidently. 2020 Mar 18;21(1):110. doi: 10.1186/s12859-020-3433-x. RNA-seq is instrumental in understanding the complexity of the transcriptome. Somatic variants are identified by comparing allele frequencies in normal and tumor sample alignments, annotating each mutation, and aggregating mutations from multiple cases into one project file. Validation, Full List of Tools Used in this Pipeline: The samples were genotyped with the ThermoFisher Axiom Chicken Genotyping Array (the Gene Expression Omnibus Accession code GSE131764) [22]. Data curation, Muñoz-Espinoza C, Di Genova A, Sánchez A, Correa J, Espinoza A, Meneses C, Maass A, Orellana A, Hinrichsen P. BMC Plant Biol. For more information about PLOS Subject Areas, click No, Is the Subject Area "Alleles" applicable to this article? Application of the three‑caller pipeline to the whole exome data of HCC, improved the detection of true positive mutations and a total of 75 tumor‑specific somatic variants were identified. Design and evaluation of a genomics variant analysis pipeline using GATK Spark tools. PLOS ONE promises fair, rigorous peer review, To obtain higher confidence in variant calls, pooling multiple data sets (i.e. Writing – review & editing. Considering the mapping phase of RNA-seq reads is a crucial step in variant calling, we devised a reference mapping strategy using three RNA-seq splice-aware aligners to reduce the prevalence of false positives. Comprehensive Variant Analysis for Rare Genetic Disease. A true-verified SNP (TS) is a SNP with the same corresponding dbSNP and/or WGS data, and a non-verified SNP (NS) is where the genotype does not match the dbSNP/WGS data. The mutational profile of RNA-seq variants. Funding: This project was supported by Agriculture and Food Research Initiative Competitive Grants 2011-67003-30228 and 2017-67015-26543, both awarded to CJS, from the United States Department of Agriculture National institute of Food and Agriculture. https://doi.org/10.1371/journal.pone.0216838.g007, https://doi.org/10.1371/journal.pone.0216838.g008. No, PLOS is a nonprofit 501(c)(3) corporation, #C2354500, based in San Francisco, California, US, https://doi.org/10.1371/journal.pone.0216838. The source code and user manuals are available at https://modupeore.github.io/VAP/. Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data. Our software suite is designed for high-throughput labs using whole-genome sequencing to evaluate and report on variants associated with rare genetic disease. 10.1016/j.ajhg.2013.08.008 Fig 2. To determine the accuracy of detecting a true variant from RNA-seq using our VAP workflow, we calculated the specificity and sensitivity of the verified RNA-seq SNPs. Contribute to gencorefacility/covid19 development by creating an account on GitHub. https://doi.org/10.1371/journal.pone.0216838.t002. The pipeline was provided pre-installed in a dedicated computing server with an easy-to-use interface. Fig 3. Scalable and efficient processing of genome sequence data, i.e. Discover a faster, simpler path to publishing in a high-quality journal. 10.1038/nrg2626 It has been developed to work on a local high-performance computing environment or from a cloud-based … Opposum reconstructs pre-existing RNA alignment files to make them suitable for haplotype-based variant calling with Platypus [7], however no significant improvement aside runtime was observed when compared to the current widely applied approach for variant calling, which is the GATK HaplotypeCaller [4]. Precision = verifiedSNPs / (verifiedSNPs + novelSNPs). High percentages of similar SNPs were observed between all three tools, which shows that using a splice-aware read mapper is appropriate for reference mapping using RNA-seq, unlike with BWA. Specificity and number of RNA-seq…, Fig 7. Department of Animal Science, Iowa State University, Ames, Iowa, United States of America, Roles Pre-processed RNA-seq reads were mapped to the reference genome and known transcripts employing three splice-aware assembly tools; TopHat2 [12], HiSAT2 [13] and STAR [14]. BAM files are pre-processed by Picard and GATK, then merged, annotated and filtered to achieve high-confident SNPs. COVID-19 is an emerging, rapidly evolving situation. Functional enrichment analysis revealed the mutations in the genes encoding cell adhesion and regulation of Ras GTPase activity. Rare Variant Analysis Pipeline. See this image and copyright information in PMC. It also uncovers potential post-transcriptional modifications for gene regulation (Table 5) and allows for detection of previously unidentified variants that may be functionally important but difficult to capture using DNA sequencing or exome sequencing at lower cost. To calculate specificity of our VAP methodology, we focused on variants in coding regions to allow for fair comparison between RNA-seq and WGS data. Reliable Identification of Genomic Variants from RNA-Seq Data. Specificity and number of RNA-seq SNPs detected in relation to the genes expressed…, Fig 8. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Using RNA-seq data is advantageous because it enriches for expressed genic regions compared to WGS and therefore will increase the power to detect functionally important SNPs impacting protein sequence.  |  This is a static archive of our support site. Variant Analysis Pipeline for COVID19. https://doi.org/10.1371/journal.pone.0216838.t005. With some variations, variant discovery consists of a pipeline where data ows through a number of well-understood steps, from the raw reads o the sequencing machine, to a list of functionally annotated variants that can be interpreted by a clinician. The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Yes Nat Rev Genet. https://doi.org/10.1371/journal.pone.0216838.g005. For WGS, pooled DNA samples were constructed from individual DNA isolates from blood from 16 birds, contributing to 241 million 100bp pair-end reads (Fleming et al., 2016; the NCBI Sequence Read Archive Accession number SRP192622) [21]. Given the high accuracy of genotyping arrays for SNP discovery, we compared our initially verified RNA-seq SNPs with the genotyped chromosomes identified in the 600k chicken genotyping panel (i.e. The decreased precision in heterozygous SNPs may suggest expression of the non-reference allele, and this provides the opportunity to study the effects of genetic variation on the different transcriptional events, such as RNA editing, alternate splicing and allelic specific expression, which cannot be explained using DNA sequencing data [31]. Vap allows the detection of structural variants basing on 30X PCR-free WGS quality variants from transcriptome sequencing.! 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