YGENO-08702; No. of pages: 6; 4C: Genomics xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Genomics journal homepage: www.elsevier.com/locate/ygeno
Methods
2Q1
VennBLAST—Whole transcriptome comparison and visualization tool
3Q2
Tamar Zahavi a, Gil Stelzer b, Lior Strauss a, Asher Y. Salmon c, Mali Salmon-Divon a,⁎
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Article history: Received 24 August 2014 Accepted 16 December 2014 Available online xxxx
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Keywords: Venn-diagrams BLAST Transcriptomics RNA-seq
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RNA-seq is the method of choice for getting a primary list of genes for non-model organisms. Once this is achieved, one would proceed to annotate the newly discovered genes and consequently strive to position the organism in an evolutionary context. These kinds of studies involving high-throughput sequencing generate large amounts of data, whose analysis might be time consuming for the non-specialist user and merit computational skills. Here we describe VennBLAST, a set of high-performance utilities that combines fast parallelized BLAST filtering with a visualization tool for whole-transcriptomic alignment comparison using Venn diagrams. The software accurately illustrates simple set relationships between numbers of matching sequences and identifies transcriptome conservation among different organisms. The intuitive Venn diagram visualization allows researchers to easily select a desired subset of genes for further inspection, using the DAVID functional annotation tools, for instance, which enables investigators to understand biological meaning behind large lists of genes. © 2014 Published by Elsevier Inc.
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Department of Molecular Biology, Ariel University, Ariel, Israel Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel Barzilai Medical Center, Ashkelon, Israel
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1. Introduction
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High throughput sequencing has become a widespread technology and greatly accessible to various research fields including evolutionary studies. Indeed, the ability to sequence and annotate transcriptomes of organisms whose genomes are not available has become a routine task for biologists in the field of molecular evolution, specifically involving non-model organisms. A detailed walkthrough and pipeline for analysis of non-model organisms' population genomics via RNA-Seq was described by De Wit [1]. The first step in this process is the de-novo assembly of a large number of sequences to a set of transcript contigs. This can be done using different tools such as Trinity [2], Trans-ABySS [3], Velvet-Oasis [4], MIRA [5], Newbler for 454 reads [6] etc. The next important step in the process is the functional annotation of the assembled contigs which can be done using several software suites such as Blast2Go [7], Trinotate [8], T-Ace [9], annot8r [10], FastAnnotator [11] etc. These tools make use of different well-referenced methods for homology searches to known sequence databases, protein domain identification, GO terms and pathway analysis, all relying largely on BLAST-like analysis. BLAST (Basic Local Alignment Search Tool) [12] is the most widely used set of programs for finding regions of local similarity between sequences, including annotation of assembled ESTs originating from transcriptome sequencing of unknown genome organisms. While, BLAST results show the levels of similarity between the query sequence and each of the individual matches (subjects), it lacks the ability to
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⁎ Corresponding author. E-mail address: [email protected] (M. Salmon-Divon).
achieve whole transcriptome BLAST search insights. Typically, one could determine the degree of evolutionary conservation amongst species, deduce the pathway landscape available in the studied species or alternatively compare inter-species expression patterns. Establishing differential expression profiles between two or more sets of EST databases can be achieved using a pipeline consisting of an assembly software and digital gene expression statistical tool such as DESeq [13] and edgeR [14]. A protocol using Trinity and edgeR tools is described on the Trinity website [2], which requires compilation of a consensus transcriptome as a prerequisite. T-Ace offers a statistical comparison between transcriptomes; this is however also dependent on a consensus transcriptome assembled by the interrogation of the raw expression data from several species. While major efforts have been put into the development of assembly and annotation software as well as expression analysis, downstream investigation such as whole transcriptome comparisons is much less available. Here we present VennBLAST, an integrated, user-friendly software that combines a fast parallelized BLAST filtering utility with whole-transcriptomic alignment comparison. VennBLAST depicts the results in an intuitive Venn diagram, illustrating simple set relationships between numbers of BLAST comparisons. VennBLAST provides a birdseye view of the evolutionary relatedness of whole-transcriptomes, but also enables the dissection of genes into meaningful subgroups which can then be further analyzed using various tools. Gene set enrichment analysis, for example, can be implemented on VennBLAST selected gene lists in order to understand the biological meaning behind them. While performing these tasks might be straight-forward for bioinformaticians, they are time consuming for non-specialists, the target audience of VennBLAST.
http://dx.doi.org/10.1016/j.ygeno.2014.12.004 0888-7543/© 2014 Published by Elsevier Inc.
Please cite this article as: Tamar Zahavi, et al., VennBLAST—Whole transcriptome comparison and visualization tool, Genomics (2014), http:// dx.doi.org/10.1016/j.ygeno.2014.12.004
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VennBLAST is configured to run as a desktop application with an interactive user interface. It is implemented in C# programming language, and uses the “Venn diagram plotter” code [15] for drawing Venn diagrams. VennBLAST is freely available for non-commercial use at http:// www.ariel.ac.il/research/fbl/software.
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2.1. Program descriptions
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In order to demonstrate the “Filter” utility of VennBLAST, we used the data published by Karako-Lampert et al. [16], whereby a single assembled transcriptome of a non-model organism, Stylophora pistillata coral, was interrogated against several established Cnidaria transcriptomes from the Scleractinia, Actiniaria and Hydrozoa orders (as described in [16]). We used the ‘Filter’ option of VennBLAST in order to keep best hits (Identity N 70%, Alignment length N =100, E value b 1e − 10). The filtration was carried out separately for each BLAST output file, per organism, reducing the gene count by 97 percent on average (Fig. 2). The effectiveness of the VennBLAST “Merge” feature as a broad utility tool was demonstrated with two different comparisons, the first featuring a “one to many” example and the second a “many to one” comparison. Both demonstrate the power of VennBLAST to process a multitude of data matrices at once. The first (“one to many”) used the transcriptomic data of the S. pistillata coral (as described in the ‘Filter’ illustration section above). Contigs of a single non-model organism, in this case, were used as queries against various known species' genes (subjects) belonging to three orders that served as main (inter) groups, Scleractinia, Actinaria and Hydrozoans, each containing 6, 4 and 3 intra group species, respectively. Prior to Venn diagram creation, genes were unified within each main group based on common query IDs. Next, the Venn diagram was created (Fig. 3) by comparing the three inter/main groups based on the IDs common to all (again query IDs in this example). In agreement with the published results, we found that the least amount of cross matches were obtained among the hydrozoans, which are more divergent, and the highest conservation was exhibited to the Scleractinia order, as expected, since S. pistillata is a stony coral (for additional transcriptome data for this species see [17]). As demonstrated in the example above, VennBLAST can produce an overview of a single transcriptome by its relations to other organisms. The organisms for comparison should be judiciously chosen based on the study focus. For example, if evolutionarily conserved genes are the research focus, then one might select organisms further away on the phylogenetic tree. However, if one is interested in finding nuances responsible for adaptations to specific niches then comparing closely related species is more suitable. Contrary to the previous example using a single non-model organism against several known species, we now compared multiple non-model organisms against a single species at a time (“many to one”). For illustration, we used the data published by Riesgo et al. [18] which provides a comparative analysis of de-novo assembled transcriptomic data for ten non-model species belonging to five animal phyla (2 Annelida [including Sipuncula], 2 Arthropoda, 2 Mollusca, 2 Nemertea, and 2 Porifera). cDNA libraries of these species were sequenced in different batches with an Illumina Genome Analyzer II and between 67,423 and 207,559 contigs were obtained across these species, post-optimization. Based on these previously published data, transcript contigs for each of these species were compared to Human, Mouse and Drosophila “whole proteome” databases (Uniprot + SwissProt, downloaded from http://www.uniprot.org/downloads [19]) using BLASTX, which compares translated nucleotide query sequences against a protein database. We used the ‘Filter’ option of VennBLAST in order to keep only accurate hits (Identity ≥ 50%, E value b 1e − 10, Coverage ≥ 50). The filtration was carried out separately for each BLASTX output file, per organism, reducing the gene count by 84 percent on average.
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2.1.1. Filter ‘Filter’ allows the filtering of BLAST output files so that highly accurate hits will be preserved based on the different BLAST alignment parameters. Before running BLAST, one can adjust parameters aiming to tackle irrelevant short hits, such as using the Best-Hit algorithm (-best_hit_overhang and –best_hit_score_edge parameters). In addition to pre-run short-hit reduction parameters, BLAST exposes several parameters that allow the user to adjust the output stringency; however the task of finding the optimal parameter set is non-trivial and can be needlessly time consuming if it requires running the software several times. The VennBLAST ‘Filter’ utility filters non-stringent BLAST matches allowing the user to optimize alignment stringency without requiring the user to optimize BLAST parameters. This optimization essentially balances between the sensitivity and specificity. Usually increasing sensitivity, that attempts to detect all true positives, conversely affects specificity, which attempts to present all true negatives. Depending on the research objective, a tradeoff threshold, represented by several BLAST parameters, should be selected, such as E-value, alignment score, % identity, and refined until satisfaction, thus reducing BLAST fine-tuning. The most significant parameter to be adjusted is the E-value, representing the number of hits one can “expect” to see by chance when searching a database of the same size. We recommend lowering the BLAST default E-value from 10 to 10−5 at most. However, biologically irrelevant hits might still pass this threshold therefore warranting the use of additional filtering parameters, such as increasing % identity, especially when comparing closely related species. Another important parameter to be considered in the filtering step is the query coverage which describes the proportion of the query length that was aligned. When comparing closely related species one would increase the filtered coverage in order to ignore sequences that share short local motifs and are not necessarily true orthologs. The more distant the species being compared, the more relaxed can this parameter be.
2.1.2. Merge ‘Merge’ is the VennBLAST feature responsible for the actual BLAST output comparisons and their visualization. It is designed to give maximal flexibility to the user. In its simplest form, one can compare a given organism's transcriptome (usually non-model) with two additional organisms' expressed genes and receive a graphical representation of those unique or common to all. This can also be expanded to include three main (inter) groups. ‘Merge’, however, is much more robust since one can first combine multiple organisms' transcriptomes (namely intra groups) and then continue to compare them as a whole to the desired main groups. Using a few representatives within each main group ensures a reliable depiction of the degree of similarity between the non-model organism and a phylogenetic clade. Additionally, in order to increase the gene pool representing each main group, adding more than a single organism to each suspected group is suggested. BLAST output files within main group can be combined, prior to main group comparison, in two modes: ‘intersection’, which keeps only hits found within each main group members, and ‘union’ that keeps all alignment hits including those unique to a specific member. In order to avoid redundancy of many subjects being associated with
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VennBLAST comprises two main utilities: ‘Filter’ and ‘Merge’.
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the same query, the user can choose to keep only the first hit (usually the most significant one) for each query sequence in each input file. A schematic illustration of three typical workflows one should follow for creating the final Venn diagrams appears in Fig. 1. The output is visualized as a Venn diagram with the option to export all Venn-diagram lists to excel. All queries and subjects of the various Venn diagram slices are exported, and can be directed to functional annotation tools for additional biological inspection. An algorithm description and pseudocode appears in the supplementary materials.
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Subsequently, we compared the transcriptomic data of the ten nonmodel invertebrates species to that of Human, Mouse and Drosophila by using the ‘Merge’ utility of VennBLAST. In this experimental design we have three main-groups. Each is comprised of ten non-model organisms compared using BLASTX against the selected known species. Since the common IDs of all the three main groups are the contig names of the non-model species, the comparison between main (inter) groups was done based on query IDs. Each main group contains 10 filtered BLASTX files as described above, one for each of the non-model species. Since non model ESTs (query) were all compared using BLASTX against a single database (subject), the merging within each main group was done based on the union or intersected subject IDs. The used parameters as well as the output Venn diagrams are illustrated in Fig. 4 (A–B). In order to glean biological insights with an evolutionary perspective, genes assembled into meaningful subsets by VennBLAST may be tested for biological processes, pathways and molecular function enrichment. The 34635 contig IDs (belonging to 10491 known genes) shared most by all three main groups (Human, Mouse or Drosophilla) (Fig. 4A) were submitted to the DAVID functional annotation tool [20]. These were found to be enriched with GO terms associated with basic biological processes such as transcription, translation, RNA processing etc. Gene ontology analysis of the 3960 invertebrates contigs (belongs to 2283 known genes) having orthologs common only to Drosophila, was enriched with GO terms such as, establishment of ommatidial polarity, open tracheal system development, instar larval or pupal development, etc. The outlined developmental processes are expected to be shared by
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Fig. 1. A schematic illustration of three typical workflows one should follow for creating the final Venn diagrams.
these lower invertebrate organisms and fly and less common to organisms appearing later in evolution. Comparison of the ten non-model transcriptomes with that of the fruit fly based on “intersection” analysis (Fig. 4B), revealed that approximately 40% of all drosophila orthologs (1696/4243) are solely conserved between the non-model organisms and drosophila, compared to 29% and 17% in mouse and human, respectively. That is, these non-model organisms and fly appear to share a core set of unique transcripts, recapitulating that the evolutionary distance between them is shorter than that of mouse and human [21,22].
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3. Discussion and conclusions
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As next generation sequencing becomes more affordable, RNA-Seq is increasingly attractive for carrying out comparative genomic studies. This is especially powerful in non-model species, for which there is often much knowledge of the evolution and ecology but little or no genomic resources. RNA-Seq empowers researchers to answer transcriptomicbased questions for non-model organisms, including differential expression analysis, novel gene identification and in the context of evolutionary studies, the estimation of the distance between sets of non-model and known species transcriptomes. The later can be done using VennBLAST which allows an unbiased impression and convenient visualization of the evolutionary proximity between sets of transcriptomes by interrogation of BLAST annotations. A common approach for studying phylogenetic relatedness of an unknown organism with others, is to isolate a known
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glimpse. However, it is intended to complement detailed phylogenetic analysis rather than replace it. VennBLAST relies on BLAST output; hence it processes any type of sequence alignment such as BLASTN (for nucleotide), BLASTP (for protein) and BLASTX (for translated sequences). Hence it offers users the
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gene and compare it to its orthologs. Rather than focusing on one or a small number of genes as a representation of the organism, the rationale behind VennBLAST involves multi gene homology across species. VennBLAST empowers users to get a picture of how a non-model organism is connected to several presumably related groups of species, in one
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Fig. 2. VennBLAST ‘Filter’ utility. Filtration was carried out separately for each BLAST output file, keeping only those hits that pass the different filtering parameters. The number of hits left after filtering is indicated in the “Filter output” box.
Fig. 3. Using VennBLAST ‘Merge utility’ to compare three groups of whole transcriptome BLAST searches using the ‘union’ mode: group A (pink) represents alignment of Stylophora pistillata coral against Cnidaria transcriptomes from the Scleractinia order, group B (light blue) represents alignment against transcriptomes from the Actiniaria order, and group C (yellow) against transcriptomes from the Hydrozoa class. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Tamar Zahavi, et al., VennBLAST—Whole transcriptome comparison and visualization tool, Genomics (2014), http:// dx.doi.org/10.1016/j.ygeno.2014.12.004
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Fig. 4. Using VennBLAST ‘Merge utility’ to compare three groups of whole transcriptome BLAST searches using two modes: ‘union’ (A) and ‘intersection’ (B) Group A (pink) represents alignment of non-model worms against human, group B (light blue) alignment against mouse, and group C (yellow) against drosophila. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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flexibility of comparing against any database of interest, but still it is limited to BLAST. This system-wide approach, in concordance with international efforts such as the GIGA project [23], establishes the degree of commonality between the species of interest but more importantly can delineate the biological processes which might be responsible for the observed conservation. VennBLAST greatly simplifies the highlighting of pathways and molecular functions, which have been essential over evolution, via the genes common to compared groups. Arranging whole transcriptome information into gene subsets, demonstrated by VennBLAST, provides
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Acknowledgments
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The authors acknowledged Dr. Ana Riesgo (Department of Organismic and Evolutionary Biology, Harvard University) for kindly providing transcriptome assemblies for the ten non-model organisms used in the illustration section.
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Contents lists available at ScienceDirect
Genomics journal homepage: www.elsevier.com/locate/ygeno
Methods
2Q1
VennBLAST—Whole transcriptome comparison and visualization tool
3Q2
Tamar Zahavi a, Gil Stelzer b, Lior Strauss a, Asher Y. Salmon c, Mali Salmon-Divon a,⁎
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Article history: Received 24 August 2014 Accepted 16 December 2014 Available online xxxx
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Keywords: Venn-diagrams BLAST Transcriptomics RNA-seq
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RNA-seq is the method of choice for getting a primary list of genes for non-model organisms. Once this is achieved, one would proceed to annotate the newly discovered genes and consequently strive to position the organism in an evolutionary context. These kinds of studies involving high-throughput sequencing generate large amounts of data, whose analysis might be time consuming for the non-specialist user and merit computational skills. Here we describe VennBLAST, a set of high-performance utilities that combines fast parallelized BLAST filtering with a visualization tool for whole-transcriptomic alignment comparison using Venn diagrams. The software accurately illustrates simple set relationships between numbers of matching sequences and identifies transcriptome conservation among different organisms. The intuitive Venn diagram visualization allows researchers to easily select a desired subset of genes for further inspection, using the DAVID functional annotation tools, for instance, which enables investigators to understand biological meaning behind large lists of genes. © 2014 Published by Elsevier Inc.
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1. Introduction
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High throughput sequencing has become a widespread technology and greatly accessible to various research fields including evolutionary studies. Indeed, the ability to sequence and annotate transcriptomes of organisms whose genomes are not available has become a routine task for biologists in the field of molecular evolution, specifically involving non-model organisms. A detailed walkthrough and pipeline for analysis of non-model organisms' population genomics via RNA-Seq was described by De Wit [1]. The first step in this process is the de-novo assembly of a large number of sequences to a set of transcript contigs. This can be done using different tools such as Trinity [2], Trans-ABySS [3], Velvet-Oasis [4], MIRA [5], Newbler for 454 reads [6] etc. The next important step in the process is the functional annotation of the assembled contigs which can be done using several software suites such as Blast2Go [7], Trinotate [8], T-Ace [9], annot8r [10], FastAnnotator [11] etc. These tools make use of different well-referenced methods for homology searches to known sequence databases, protein domain identification, GO terms and pathway analysis, all relying largely on BLAST-like analysis. BLAST (Basic Local Alignment Search Tool) [12] is the most widely used set of programs for finding regions of local similarity between sequences, including annotation of assembled ESTs originating from transcriptome sequencing of unknown genome organisms. While, BLAST results show the levels of similarity between the query sequence and each of the individual matches (subjects), it lacks the ability to
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⁎ Corresponding author. E-mail address: [email protected] (M. Salmon-Divon).
achieve whole transcriptome BLAST search insights. Typically, one could determine the degree of evolutionary conservation amongst species, deduce the pathway landscape available in the studied species or alternatively compare inter-species expression patterns. Establishing differential expression profiles between two or more sets of EST databases can be achieved using a pipeline consisting of an assembly software and digital gene expression statistical tool such as DESeq [13] and edgeR [14]. A protocol using Trinity and edgeR tools is described on the Trinity website [2], which requires compilation of a consensus transcriptome as a prerequisite. T-Ace offers a statistical comparison between transcriptomes; this is however also dependent on a consensus transcriptome assembled by the interrogation of the raw expression data from several species. While major efforts have been put into the development of assembly and annotation software as well as expression analysis, downstream investigation such as whole transcriptome comparisons is much less available. Here we present VennBLAST, an integrated, user-friendly software that combines a fast parallelized BLAST filtering utility with whole-transcriptomic alignment comparison. VennBLAST depicts the results in an intuitive Venn diagram, illustrating simple set relationships between numbers of BLAST comparisons. VennBLAST provides a birdseye view of the evolutionary relatedness of whole-transcriptomes, but also enables the dissection of genes into meaningful subgroups which can then be further analyzed using various tools. Gene set enrichment analysis, for example, can be implemented on VennBLAST selected gene lists in order to understand the biological meaning behind them. While performing these tasks might be straight-forward for bioinformaticians, they are time consuming for non-specialists, the target audience of VennBLAST.
http://dx.doi.org/10.1016/j.ygeno.2014.12.004 0888-7543/© 2014 Published by Elsevier Inc.
Please cite this article as: Tamar Zahavi, et al., VennBLAST—Whole transcriptome comparison and visualization tool, Genomics (2014), http:// dx.doi.org/10.1016/j.ygeno.2014.12.004
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VennBLAST is configured to run as a desktop application with an interactive user interface. It is implemented in C# programming language, and uses the “Venn diagram plotter” code [15] for drawing Venn diagrams. VennBLAST is freely available for non-commercial use at http:// www.ariel.ac.il/research/fbl/software.
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In order to demonstrate the “Filter” utility of VennBLAST, we used the data published by Karako-Lampert et al. [16], whereby a single assembled transcriptome of a non-model organism, Stylophora pistillata coral, was interrogated against several established Cnidaria transcriptomes from the Scleractinia, Actiniaria and Hydrozoa orders (as described in [16]). We used the ‘Filter’ option of VennBLAST in order to keep best hits (Identity N 70%, Alignment length N =100, E value b 1e − 10). The filtration was carried out separately for each BLAST output file, per organism, reducing the gene count by 97 percent on average (Fig. 2). The effectiveness of the VennBLAST “Merge” feature as a broad utility tool was demonstrated with two different comparisons, the first featuring a “one to many” example and the second a “many to one” comparison. Both demonstrate the power of VennBLAST to process a multitude of data matrices at once. The first (“one to many”) used the transcriptomic data of the S. pistillata coral (as described in the ‘Filter’ illustration section above). Contigs of a single non-model organism, in this case, were used as queries against various known species' genes (subjects) belonging to three orders that served as main (inter) groups, Scleractinia, Actinaria and Hydrozoans, each containing 6, 4 and 3 intra group species, respectively. Prior to Venn diagram creation, genes were unified within each main group based on common query IDs. Next, the Venn diagram was created (Fig. 3) by comparing the three inter/main groups based on the IDs common to all (again query IDs in this example). In agreement with the published results, we found that the least amount of cross matches were obtained among the hydrozoans, which are more divergent, and the highest conservation was exhibited to the Scleractinia order, as expected, since S. pistillata is a stony coral (for additional transcriptome data for this species see [17]). As demonstrated in the example above, VennBLAST can produce an overview of a single transcriptome by its relations to other organisms. The organisms for comparison should be judiciously chosen based on the study focus. For example, if evolutionarily conserved genes are the research focus, then one might select organisms further away on the phylogenetic tree. However, if one is interested in finding nuances responsible for adaptations to specific niches then comparing closely related species is more suitable. Contrary to the previous example using a single non-model organism against several known species, we now compared multiple non-model organisms against a single species at a time (“many to one”). For illustration, we used the data published by Riesgo et al. [18] which provides a comparative analysis of de-novo assembled transcriptomic data for ten non-model species belonging to five animal phyla (2 Annelida [including Sipuncula], 2 Arthropoda, 2 Mollusca, 2 Nemertea, and 2 Porifera). cDNA libraries of these species were sequenced in different batches with an Illumina Genome Analyzer II and between 67,423 and 207,559 contigs were obtained across these species, post-optimization. Based on these previously published data, transcript contigs for each of these species were compared to Human, Mouse and Drosophila “whole proteome” databases (Uniprot + SwissProt, downloaded from http://www.uniprot.org/downloads [19]) using BLASTX, which compares translated nucleotide query sequences against a protein database. We used the ‘Filter’ option of VennBLAST in order to keep only accurate hits (Identity ≥ 50%, E value b 1e − 10, Coverage ≥ 50). The filtration was carried out separately for each BLASTX output file, per organism, reducing the gene count by 84 percent on average.
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2.1.1. Filter ‘Filter’ allows the filtering of BLAST output files so that highly accurate hits will be preserved based on the different BLAST alignment parameters. Before running BLAST, one can adjust parameters aiming to tackle irrelevant short hits, such as using the Best-Hit algorithm (-best_hit_overhang and –best_hit_score_edge parameters). In addition to pre-run short-hit reduction parameters, BLAST exposes several parameters that allow the user to adjust the output stringency; however the task of finding the optimal parameter set is non-trivial and can be needlessly time consuming if it requires running the software several times. The VennBLAST ‘Filter’ utility filters non-stringent BLAST matches allowing the user to optimize alignment stringency without requiring the user to optimize BLAST parameters. This optimization essentially balances between the sensitivity and specificity. Usually increasing sensitivity, that attempts to detect all true positives, conversely affects specificity, which attempts to present all true negatives. Depending on the research objective, a tradeoff threshold, represented by several BLAST parameters, should be selected, such as E-value, alignment score, % identity, and refined until satisfaction, thus reducing BLAST fine-tuning. The most significant parameter to be adjusted is the E-value, representing the number of hits one can “expect” to see by chance when searching a database of the same size. We recommend lowering the BLAST default E-value from 10 to 10−5 at most. However, biologically irrelevant hits might still pass this threshold therefore warranting the use of additional filtering parameters, such as increasing % identity, especially when comparing closely related species. Another important parameter to be considered in the filtering step is the query coverage which describes the proportion of the query length that was aligned. When comparing closely related species one would increase the filtered coverage in order to ignore sequences that share short local motifs and are not necessarily true orthologs. The more distant the species being compared, the more relaxed can this parameter be.
2.1.2. Merge ‘Merge’ is the VennBLAST feature responsible for the actual BLAST output comparisons and their visualization. It is designed to give maximal flexibility to the user. In its simplest form, one can compare a given organism's transcriptome (usually non-model) with two additional organisms' expressed genes and receive a graphical representation of those unique or common to all. This can also be expanded to include three main (inter) groups. ‘Merge’, however, is much more robust since one can first combine multiple organisms' transcriptomes (namely intra groups) and then continue to compare them as a whole to the desired main groups. Using a few representatives within each main group ensures a reliable depiction of the degree of similarity between the non-model organism and a phylogenetic clade. Additionally, in order to increase the gene pool representing each main group, adding more than a single organism to each suspected group is suggested. BLAST output files within main group can be combined, prior to main group comparison, in two modes: ‘intersection’, which keeps only hits found within each main group members, and ‘union’ that keeps all alignment hits including those unique to a specific member. In order to avoid redundancy of many subjects being associated with
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VennBLAST comprises two main utilities: ‘Filter’ and ‘Merge’.
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the same query, the user can choose to keep only the first hit (usually the most significant one) for each query sequence in each input file. A schematic illustration of three typical workflows one should follow for creating the final Venn diagrams appears in Fig. 1. The output is visualized as a Venn diagram with the option to export all Venn-diagram lists to excel. All queries and subjects of the various Venn diagram slices are exported, and can be directed to functional annotation tools for additional biological inspection. An algorithm description and pseudocode appears in the supplementary materials.
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Subsequently, we compared the transcriptomic data of the ten nonmodel invertebrates species to that of Human, Mouse and Drosophila by using the ‘Merge’ utility of VennBLAST. In this experimental design we have three main-groups. Each is comprised of ten non-model organisms compared using BLASTX against the selected known species. Since the common IDs of all the three main groups are the contig names of the non-model species, the comparison between main (inter) groups was done based on query IDs. Each main group contains 10 filtered BLASTX files as described above, one for each of the non-model species. Since non model ESTs (query) were all compared using BLASTX against a single database (subject), the merging within each main group was done based on the union or intersected subject IDs. The used parameters as well as the output Venn diagrams are illustrated in Fig. 4 (A–B). In order to glean biological insights with an evolutionary perspective, genes assembled into meaningful subsets by VennBLAST may be tested for biological processes, pathways and molecular function enrichment. The 34635 contig IDs (belonging to 10491 known genes) shared most by all three main groups (Human, Mouse or Drosophilla) (Fig. 4A) were submitted to the DAVID functional annotation tool [20]. These were found to be enriched with GO terms associated with basic biological processes such as transcription, translation, RNA processing etc. Gene ontology analysis of the 3960 invertebrates contigs (belongs to 2283 known genes) having orthologs common only to Drosophila, was enriched with GO terms such as, establishment of ommatidial polarity, open tracheal system development, instar larval or pupal development, etc. The outlined developmental processes are expected to be shared by
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Fig. 1. A schematic illustration of three typical workflows one should follow for creating the final Venn diagrams.
these lower invertebrate organisms and fly and less common to organisms appearing later in evolution. Comparison of the ten non-model transcriptomes with that of the fruit fly based on “intersection” analysis (Fig. 4B), revealed that approximately 40% of all drosophila orthologs (1696/4243) are solely conserved between the non-model organisms and drosophila, compared to 29% and 17% in mouse and human, respectively. That is, these non-model organisms and fly appear to share a core set of unique transcripts, recapitulating that the evolutionary distance between them is shorter than that of mouse and human [21,22].
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As next generation sequencing becomes more affordable, RNA-Seq is increasingly attractive for carrying out comparative genomic studies. This is especially powerful in non-model species, for which there is often much knowledge of the evolution and ecology but little or no genomic resources. RNA-Seq empowers researchers to answer transcriptomicbased questions for non-model organisms, including differential expression analysis, novel gene identification and in the context of evolutionary studies, the estimation of the distance between sets of non-model and known species transcriptomes. The later can be done using VennBLAST which allows an unbiased impression and convenient visualization of the evolutionary proximity between sets of transcriptomes by interrogation of BLAST annotations. A common approach for studying phylogenetic relatedness of an unknown organism with others, is to isolate a known
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glimpse. However, it is intended to complement detailed phylogenetic analysis rather than replace it. VennBLAST relies on BLAST output; hence it processes any type of sequence alignment such as BLASTN (for nucleotide), BLASTP (for protein) and BLASTX (for translated sequences). Hence it offers users the
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gene and compare it to its orthologs. Rather than focusing on one or a small number of genes as a representation of the organism, the rationale behind VennBLAST involves multi gene homology across species. VennBLAST empowers users to get a picture of how a non-model organism is connected to several presumably related groups of species, in one
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Fig. 2. VennBLAST ‘Filter’ utility. Filtration was carried out separately for each BLAST output file, keeping only those hits that pass the different filtering parameters. The number of hits left after filtering is indicated in the “Filter output” box.
Fig. 3. Using VennBLAST ‘Merge utility’ to compare three groups of whole transcriptome BLAST searches using the ‘union’ mode: group A (pink) represents alignment of Stylophora pistillata coral against Cnidaria transcriptomes from the Scleractinia order, group B (light blue) represents alignment against transcriptomes from the Actiniaria order, and group C (yellow) against transcriptomes from the Hydrozoa class. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Please cite this article as: Tamar Zahavi, et al., VennBLAST—Whole transcriptome comparison and visualization tool, Genomics (2014), http:// dx.doi.org/10.1016/j.ygeno.2014.12.004
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Fig. 4. Using VennBLAST ‘Merge utility’ to compare three groups of whole transcriptome BLAST searches using two modes: ‘union’ (A) and ‘intersection’ (B) Group A (pink) represents alignment of non-model worms against human, group B (light blue) alignment against mouse, and group C (yellow) against drosophila. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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flexibility of comparing against any database of interest, but still it is limited to BLAST. This system-wide approach, in concordance with international efforts such as the GIGA project [23], establishes the degree of commonality between the species of interest but more importantly can delineate the biological processes which might be responsible for the observed conservation. VennBLAST greatly simplifies the highlighting of pathways and molecular functions, which have been essential over evolution, via the genes common to compared groups. Arranging whole transcriptome information into gene subsets, demonstrated by VennBLAST, provides
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The authors acknowledged Dr. Ana Riesgo (Department of Organismic and Evolutionary Biology, Harvard University) for kindly providing transcriptome assemblies for the ten non-model organisms used in the illustration section.
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