Next-generation sequencing (NGS) is expected to have a tremendous impact on genomic research. NGS methodology has the ability to sequence DNA or RNA fragments with extraordinary speed that has never been achieved before and therefore enables the development of original applications, accelerating novel scientific discoveries (1). In fact, genome sequencing projects that previously required numerous Sanger sequencing experiments can now be accomplished in a couple of days with a cost reduction of many thousand times, due to the massive parallel sequencing that NGS offers (2). NGS can be an extremely useful tool, used to uncover the complexity of the human genome as well as genetic alterations in a cancer genome, or specific single nucleotide polymorphisms (SNPs) that may possess a vital role in many diseases such as cancer. Since many genetic variants which contribute to many human conditions are still unknown, NGS will definitely contribute to the identification of these genetic variants, including single nucleotide variants (SNVs) or SNPs, small insertions and deletions (indels, 1–1,000 bp), structural and genomic variants (>1,000 bp) (3).
Except from genetic variations, NGS can also unveil information regarding alternative splicing events or transcript identifications (4,5). It should be noticed that with the aid of high-throughput sequencing, the identification of low-frequency variants is now feasible. This innovative ability that NGS technology provides is of high significance because the existence, or not, of a specific splice variant constitutes vital information with potential clinical value in many diseases. For example, studies in LnCaP cell line revealed that the overexpression of short TRMP8α (sM8α), a specific alternative transcript of transient receptor potential melastatin 8 (TRPM8), not only can lead to the promotion of cell migration and invasion, but also to the mitigation of starvation-induced apoptosis (6). In addition, high-throughput sequencing technology of NGS can divulge various alternative transcripts with predictive value which could be used as potential biomarkers, thus enabling a better diagnosis or prognosis for many diseases. Recent evidence regarding the B cell CLL/lymphoma 11A (BCL11A) gene, revealed that a specific transcript, BCL11A-XL, is an independent predictor of relapse in cases of squamous cell carcinomas and large cell carcinomas, thus possessing biomarker capabilities (7).
Despite its tremendous power and significant advantages, NGS demonstrates two serious limitations: short reads that provide information for a limited nucleotide region as well as the massive output data that is produced. Each NGS run can provide researchers with an enormous amount of raw data that makes both storage and analysis hard to accomplish. These facts suggest that advanced bioinformatics tools have become not only a necessity, but a priority, in order for NGS technology to be used efficiently and solve unanswered questions on genomic research that previous technologies were incapable of solving (8). The manipulation and analysis of sequencing data acquired by NGS platforms regarding alternative splicing events are probably the most challenging part of the whole sequencing workflow. For this reason, we designed a suitable algorithm that efficiently and automatically detects and identifies alternative splicing events that exist in high-throughput sequencing data. As a result, our algorithm “Alternative Splicing Detection Tool (ASDT)” is a script written in PERL that is capable of identifying alternative splicing events, providing the user with both qualitative and quantitative results regarding alternative splicing in the obtained NGS data. ASDT is available as a PERL script at http://aias.biol.uoa.gr/~mtheo, along with ReadMe.pdf file that contains detailed instructions on how to run ASDT as well as example input files for a tutorial run.
Necessary input files for ASDT
ASDT was designed to detect both annotated and novel alternative splicing events in high-throughput NGS data along with their frequency, providing the number of sequencing reads corresponding to each splicing event. In addition, ASDT requires two input files for a successful run. The first input file is the full nucleotide GenBank® report of the gene of interest that can be downloaded from the GenBank® sequence database, which is an open-access, annotated collection of all publicly available nucleotide sequences and the respective proteins. This file contains information about all the annotated messenger RNAs (mRNAs), miscellaneous RNAs (miscRNAs) as well as non-coding RNAs (ncRNAs) that are transcribed from the gene of interest. Detailed instructions on how GenBank® report file can be downloaded are shown in ReadMe.pdf. The second input file is the raw data of the NGS run (FASTQ file). FASTQ format is a text-based format for storing both a nucleotide sequence and its corresponding quality scores. In FASTQ files each record consists of four consecutive lines. The first line is the ID of the sequencing read, the second line is the sequence read, the third line contains the symbol “+” and therefore does not have any biological and experimental significance, whereas the fourth line indicates the quality score for each nucleotide of the second line.
Analysis of alternative splicing events
Each ASDT run can be separated in two major parts. The first part of the analysis corresponds to the detection of annotated and novel alternative splicing events, providing the number of sequencing reads that characterize them. During this part of the analysis, ASDT uses the first input file to extract all the exon coordinates, generating in this way all exon and intron sequences from all annotated transcripts (mRNAs, miscRNAs or ncRNAs) of the gene. Then, it synthesizes nucleotide keywords for every possible alternative splicing event between all annotated exons, merging a number of bases from the 3’-end (donor) of each exon with a number of bases from the 5’-start (acceptor) of all exons that follow downstream. For each keyword that is produced, a reverse complement keyword is also synthesized, so that results regarding both strands are collected. When all nucleotide keywords are formed, ASDT tests the existence of each keyword—which in fact represents a specific splice junction—in the FASTQ file. If a match is detected, ASDT extracts in a separate file (All reads.txt) the sequence identifier, the sequencing read that represents it as it appears in the FASTQ file, the keyword that was matched and the exons that formed it (Figure 1A). Apart from reads with a simple match—therefore containing only one splicing event—ASDT also extracts in a separate output file (Multiple alternative splicing events.txt) all the sequencing reads that match multiple nucleotide keywords, thus containing more than one splice junctions (Figure 1B).
Detection of novel cryptic exons, exon extensions or intron retentions
Regarding the second (optional) part of the analysis, ASDT can also analyze the intronic regions of the gene(s), thus enabling the detection of novel cryptic exons residing in annotated introns, extensions of previously annotated exons, or even intron retentions. During this part of the analysis, ASDT splits every intron sequence with a specific step of nucleotides, which is provided by the user in order to synthesize intronic nucleotide keywords that will cover the entire intron. Moreover, alongside every newly formed intronic sequence a reverse complement keyword will be also produced, similarly to the first part. Then, every nucleotide keyword is tested whether or not it appears in the sequencing reads of the obtained FASTQ file. Finally, each matching read that is predicted to contain novel information regarding a potential novel cryptic exon, exon extension or intron retention is printed in a separate output file, Prediction of exons-Exon extensions-Intron Retentions.txt (Figure 1C).
Parameter modifications using ASDT
ASDT provides a user-friendly environment during each run, enabling the user to modify from the command prompt many searching parameters of the run, in order to get more accurate and personalized results. For instance, the number of nucleotides that will form each keyword is chosen by the user through the command prompt. Additionally, it gives the opportunity to the user to search each nucleotide keyword in the FASTQ file not only with perfect matches, but also with mismatches, enabling the identification of splice junctions even in sequencing reads with low sequencing quality containing mismatched base(s) in the splice junction regions. The searching option using mismatches as well as the number of mismatches that will be applied in the analysis is also decided by the user through the command prompt. Furthermore, all alternative splicing events that were detected are summarized in a separate output file, providing a simple and quick overview of all the alternative splice junctions that were detected, including novel ones, as well as the number of occurrences each one was noticed (Figure 2).
Comparison of ASDT with other similar algorithms or software
Despite its power, NGS has made the research of alternative splicing hard to accomplish, due to the massive amount of data and the short sequencing reads it produces. Hundreds, thousands or even millions of short reads can be generated in experimental runs of every NGS platform (1). This fact makes the detection of rare alternative splicing events an extremely challenging procedure. As it can be concluded by many recent studies regarding alternative splicing, many novel splice junctions are significantly rare in number, compared with the annotated ones (9). In this way, their detection in NGS data without a specific tool or software is not feasible. For this reason, there have been many computational approaches and algorithms designed specifically for the analysis of alternative splicing from NGS data, but they demonstrate major differences with ASDT.
A recent software tool, Splicing Express, is able to identify patterns of simple alternative splicing events, such as exon skipping, intron retention and alternative 5' and 3'splicing borders, but it requires GTF files resulting from transcriptome assembly in order to manipulate the NGS data (10). As a result, users have to run Tophat2 or another read-aligner program and then a transcriptome assembly tool, before running Splicing Express. Other widely used bioinformatics tools for alternative splicing analysis include ABMapper (11), Comrad (12) and GSNAP (13). However, these tools demonstrate major differences with ASDT as well. ABMapper is specifically designed for exploring all putative locations of reads that are mapped to splice junctions, by using two seeds from each end of a read and searches for canonical splicing motifs (i.e., ‘GT-AG’, ‘GC-AG’ and ‘AT-AC’) to determine a stop point for spliced alignment. In addition, Comrad is designed to analyze RNA-Seq and whole genome shotgun sequencing (WGSS) data for the purposes of discovering genomic rearrangements and aberrant transcripts and therefore provides accurate classification of rearrangements as expressed or not expressed and accurate classification of the genomic or non-genomic origin of aberrant transcripts. Finally, GSNAP can detect short and long-distance splicing, including interchromosomal splicing, in individual reads, but unlike ASDT, it uses probabilistic models or a database of known splice sites. However, to the best of our knowledge ASDT is the first tool that is designed for alternative splicing analysis using only FASTQ files. Therefore, we believe that ASDT possesses some innovative and unique features that are not available in any other publicly accessible resource and has the capacity to efficiently contribute to the alternative splicing analysis of NGS data. ASDT analyzes alternative splicing events without the need for online software tools or even spliced read mappers for RNA-Seq, like TopHat2 (14). Another important feature of ASDT is its ability to detect low-frequency splice junctions, even with unique representation in the FASTQ file. This property renders this tool more sensitive than other algorithms that are currently used for this purpose. In addition, ASDT enables alternative splicing analysis without the need for comparing the annotation from GTF/GFF file, in order to identify the novel alternative splicing sites. Furthermore, users do not have to generate additional BAM/BED files and proceed to visualization using genome browsers, like UGENE (15) and IGV (16). In addition to the aforementioned, except from alternative splicing events, ASDT gives the opportunity for the user to detect potential novel exons, exon extensions or even intron retentions for the gene of interest, providing in our opinion a quick, efficient and comprehensive view of the experimental results derived from the NGS data.
Biological and clinical significance of splice variants
To date, a lot of genes are known to express alternative splice variants, some of which may have significant clinical value (17-27). The family of KLKs constitutes such a prominent example (28,29), as particular splice variants of the KLK1–KLK15 genes have been highlighted as putative biomarkers in human malignancies (30-35). Very recently, ASDT has been used to partially reveal novel transcripts of tissue kallikrein (KLK1) (36) and several other members of the kallikrein-related peptidase (KLK) family (36-39), as well as splice variants of the BCL2L12 gene (40,41), a prominent member of the apoptosis-related BCL2 family (42,43) with important prognostic value both in regard with solid tumors (44-46) as well as hematological malignancies (47,48), similarly to the major members of the BCL2 family, namely BCL2 and BAX (49-51). Moreover, the expression of a splice variant could be combined with clinical markers; for instance, the expression of BCL2L12 splice variant 1 and BCL2 variant alpha in malignant B cells of patients with chronic lymphocytic leukemia could be combined with lymphocyte count and/or other clinical features to provide a better prognostic system than the currently used ones (48,52). Similarly, expression of important transcripts resulting from alternative splicing in blasts of patients with myelodysplastic syndromes could be used along with novel markers such as monosomal karyotype to predict prognosis (53), treatment decision-making (54), or patients’ response to therapy (55,56), at is has recently been proposed.
Distinct 3’-untranslated regions (3’-UTRs) of splice variants of a single protein-coding gene resulting from alternative splicing may have other implications, including different regulatory properties. As microRNAs (miRNAs) bind 3’-UTRs to downregulate the levels of the respective proteins or protein isoforms, alterations in their sequence due to alternative splicing may account for the regulation of splice variants by distinct miRNAs (57). Thus, miRNAs regulating the expression of protein-coding transcripts subjected to intense alternative splicing may constitute very promising molecular biomarkers; such miRNAs include members of the miR-17/92 cluster (58) and its paralogue miR-106a/363, such as miR-92a-3p and miR-20b-5p as well as miR-155-5p, which have pivotal roles in other B-cell malignancies, including chronic lymphocytic leukemia (59-61). Other such important biomarkers regulating splice variants of protein-coding genes and being clinically significant include miR-15a-5p, miR-16 miR-34a, and miR-96 regulating BCL2 expression (62-65), miR-182 regulating BCL2L12 expression (66), miR-200 family members (67), as well as several other miRNAs (68-71).
As it can be easily assumed, given the vast amount of data produced by NGS, developing a massive data storage and management solution and creating bioinformatic tools to effectively analyze it will be the most crucial step for the successful application of NGS methodology (72). The full benefit of NGS will not be achieved until bioinformatics are able to maximally utilize and interpret these enormous short-read sequences into molecular and genetic useful information (73). Consequently, we believe that ASDT demonstrates many innovative and unique characteristics that can efficiently analyze alternative splicing events or identify novel cryptic exons, exon extensions or intron retentions from high-throughput NGS data.
Conflicts of Interest: The authors have no conflicts of interest to declare.
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