Inferring bona fide transfrags in RNA-Seq derived-transcriptome assemblies of non-model organisms
© Mbandi et al.; licensee BioMed Central. 2015
Received: 30 June 2014
Accepted: 6 February 2015
Published: 21 February 2015
De novo transcriptome assembly of short transcribed fragments (transfrags) produced from sequencing-by-synthesis technologies often results in redundant datasets with differing levels of unassembled, partially assembled or mis-assembled transcripts. Post-assembly processing intended to reduce redundancy typically involves reassembly or clustering of assembled sequences. However, these approaches are mostly based on common word heuristics and often create clusters of biologically unrelated sequences, resulting in loss of unique transfrags annotations and propagation of mis-assemblies.
Here, we propose a structured framework that consists of a few steps in pipeline architecture for Inferring Functionally Relevant Assembly-derived Transcripts (IFRAT). IFRAT combines 1) removal of identical subsequences, 2) error tolerant CDS prediction, 3) identification of coding potential, and 4) complements BLAST with a multiple domain architecture annotation that reduces non-specific domain annotation. We demonstrate that independent of the assembler, IFRAT selects bona fide transfrags (with CDS and coding potential) from the transcriptome assembly of a model organism without relying on post-assembly clustering or reassembly. The robustness of IFRAT is inferred on RNA-Seq data of Neurospora crassa assembled using de Bruijn graph-based assemblers, in single (Trinity and Oases-25) and multiple (Oases-Merge and additive or pooled) k-mer modes. Single k-mer assemblies contained fewer transfrags compared to the multiple k-mer assemblies. However, Trinity identified a comparable number of predicted coding sequence and gene loci to Oases pooled assembly. IFRAT selects bona fide transfrags representing over 94% of cumulative BLAST-derived functional annotations of the unfiltered assemblies. Between 4-6% are lost when orphan transfrags are excluded and this represents only a tiny fraction of annotation derived from functional transference by sequence similarity. The median length of bona fide transfrags ranged from 1.5kb (Trinity) to 2kb (Oases), which is consistent with the average coding sequence length in fungi. The fraction of transfrags that could be associated with gene ontology terms ranged from 33-50%, which is also high for domain based annotation. We showed that unselected transfrags were mostly truncated and represent sequences from intronic, untranslated (5′ and 3′) regions and non-coding gene loci.
IFRAT simplifies post-assembly processing providing a reference transcriptome enriched with functionally relevant assembly-derived transcripts for non-model organism.
Whole transcriptome analysis using next generation sequencing (NGS) or sequencing-by-synthesis (SBS) technologies offers the possibility of interrogating genes and their expression en masse without knowledge of their underlying genomes. Transcriptome sequencing is often preferred over genome sequencing because of the reduced size of the sequence target space and the high functional information content [1,2]. However, sequences generated from NGS platforms are often too short to represent entire protein-coding transcripts, and genomes for reference-guided transcriptome reconstruction are rare. De Bruijn graph assemblers allow de novo assembly of transcripts but represent only approximate computational solutions . The final assembly is one of many possibilities for which there is no universally accepted heuristic verification method; it is often highly redundant and contains mis-assemblies that are difficult to identify . Post-assembly processing intended to reduce redundancy typically involves reassembly or clustering of assembled sequences. This however may lead to propagation of mis-assemblies  and assignment of sequences to unrelated gene clusters, resulting in loss of unique annotations .
The main objective of transcriptome SBS is to ascribe functional labels to assembled transcribed fragments (transfrags). This is usually done via significant sequence similarity  or domain signature annotations . Similarity-based approaches predominantly rely on transfer of functional labels of the best BLAST hits to the sequence in question [7,9,10]. However, low BLAST annotation coverage is often observed, in particularly in transcriptomes of non-model organisms [11,12]. The implementation of significant BLAST hit as a proxy for functional annotation has further limitations: sequences that produce significant similarity may be functionally unrelated due to divergence , low complexity sequences may produce high-scoring hits but have no biological relationships , and functional homologs may lack sequence similarity . Consequently, a first large-scale assessment of protein function shows that BLAST alone is often ineffective at predicting functional labels . Domain-based annotation methods (e.g. InterProScan) appreciate only presence or absence of domains. Given that domains seldom function in isolation , a reliable approach should involve a method that recognises the overall domain co-occurrence architecture of the sequences under examination. Prerequisite for domain-based annotation is a reliable protein prediction method that tolerates sequencing errors and frame shifts.
Availability of supporting data
To establish a robust workflow for prioritizing and selecting functionally relevant (bona fide) transfrags, we selected the fungal plant pathogen Neurospora crassa  as a species with a reference genome. Publicly available non-strand specific RNA-Seq data (SRR100067) from wild type N. crassa 74-OR23-1VA was obtained from the NCBI Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/Traces/sra). Untranslated 5′ and 3′ regions were procured using Ensembl BioMart  from http://fungi.ensembl.org release-21. The associated genomic, predicted coding sequences and Rfam family genes were obtained from the Neurospora crassa Sequencing Project, Broad Institute of Harvard and MIT (http://www.broadinstitute.org). We verified the pipeline using recently published transcriptomes of non-model organisms: buckwheat (Fagopyrum esculentum) ; hydra (Hydra vulgaris) ; fresh water snail (Radix balthica) ; centipede (Alipes grandidieri), marine worm (Cerebratulus marginatus), sea cradle (Chiton olivaceus), mediterranean sponge (Crella elegans), and earthworm (Hormogaster samnitica) . The scripts, assemblies and alignment outputs generated in the ensuing analyses are available on the South Africa National Bioinformatics Institute permanent data archive (SANBI, ftp://ftp.sanbi.ac.za/ifrat).
Quality scores of ILLUMINA reads generally depreciate towards the 3′-end. Prior to assembly, low quality bases were trimmed from the 3′-end of each sequence if above an error probability of 0.01 (PHRED base quality score of 20) using a custom PERL script with snippets from ConDeTri . The quality-based filtering and trimming process ensured that orphan reads whose partner failed the quality threshold, were retained in a separate file and used for de novo transcriptome assembly.
Reference-free transcriptome reconstruction was performed separately using either Trinity (release 2012-06-08; ), or Velvet (version 1.2.03; ) in combination with Oases (version 0.2.06; ). Trinity implements greedy algorithmic traversal of the k-mer graph prior to building a de Bruign graph from clusters of pre-assembled sequences. As a result, assembled transfrags are represented by actual reads. Oases on the other-hand, interrogates a pre-assembly from Velvet to address alternative splicing and coverage variation across transcripts. Trinity was specifically designed for transcriptome assembly using a single, fixed k-mer size (k-25). Therefore we tested Oases k-25 and two variations of multiple k-mer assembly: an additive assembly by pooling (Oases-P) as described by , and a merged assembly using the Oases-merge pipeline (Oases-M). Only transfrags above 100 bp were kept for downstream analysis.
A common attribute of de novo transcriptome assemblies is sequence redundancy. Using in-house PERL or PYTHON with suffix array scripts, we filtered for 100% identical copies and subsequences (k-mer) in both, forward and reverse directions. To compare our filtering approach with a typically applied post-assembly clustering step, we used CD-HIT-EST  with the following parameters: -M 0 -T 20 -g 0 -c 1.0 - b 1 -aL 1.0 -aS 1.0 -n 10 -d 0 -p 1 (duplicate removal, +\-) and -M 0 -T 20 -g 0 -c 1.0 -b 1 -aS 1.0 -n 10 -d 0 -p 1 (substring removal, +/+). In addition, we evaluated the redundancy in each assembly using CD-HIT-EST as describe by .
Coding potential assessment and conceptual translation
Assembled transcripts were evaluated for protein-coding attributes using PORTRAIT . We corrected PORTRAIT to run ANGLE  in 6 frames, since the biological orientation of transfrags from non-strand specific libraries cannot be readily ascertained. The predicted open reading frame (ORF) with the highest dynamic programming score was chosen for conceptual translation into protein sequence using the standard codon usage table. Transfrags without an ORF were classified as orphan in this study. We note that they can be evaluated for coding capability through the protein-independent model of PORTRAIT.
We assigned protein domains to the predicted protein sequences using HMMER version 3.0  with the manually curated protein profile Hidden Markov Models from Pfam (release 26.0, ftp://ftp.sanger.ac.uk). We then applied MultiPfam2go  to explore co-occurrence relationships between the domains of each protein and assigned functional labels (gene ontology terms) if the underlying domain architectures predicted protein function.
To mimic annotation of non-model organisms, we generated a BLAST-able database of UniProt Knowledgebase (FUNGI) release 2013_02 (The UniProt Consortium: http://www.uniprot.org/), excluding N. crassa sequences. We screened for highly significant BLASTX hits (max E-value 1e-10) using the NCBI BLAST package (version 2.2.25) and identified the top hit (lowest E-value, best scoring HSP covers minimum 25% of the hit) using custom PERL scripts.
Validating bona fide transfrags by mapping to reference genome and predicted CDS
The bona fide transfrags were aligned to the reference CDS with BLAT v. 34  to assess the integrity of assembly-derived transcripts. BLAT alignment in sim4 format were generate under intron restriction (-fastMap) with -minScore = 30 and post-alignment processing were performed through a series of custom PERL scripts.
Genome-base clustering was performed to assess gene space coverage by aligning bona fide transfrags to N. crassa reference genome with GMAP 2013-09-30.v2 . The introns for N. crassa were obtained using Ensembl API  from http://fungi.ensembl.org release-17 to compute the maximum total length of intron per gene. Information about intron length statistics in fungi were obtain as described by  to parameterize transfrag and CDS alignment to the genome: min-intron length = 20, max-intron length = 2000, total length = 5904. The known gene loci are compared to the loci of aligned transfrags in a pair-wise manner using in-house PERL scripts to avoid building cluster chains . Transfrags that do not overlap with CDS are clustered using Bedtools . We aligned sequences belonging to the 5′ and 3′ untranslated regions of predicted genes and the Rfam family of predicted genes to the N. crassa genome under absolute condition of no introns using GMAP with a threshold of 95% coverage and 95% identity. The loci of these high-scoring alignments were compared to those of transfrags that did not overlap with CDS.
De novo assembly and filtering N. crassa transfrags
Quality trimming statistics of N. crassa RNA-Seq data
Processed reads pairs
Length, mean (min-max)
76 (76, 76)
72 (36, 76)
64 (36, 76)
Attributes of N. crassa assemblies produced with different filtering approaches
№ of TF (transfrags)
№ of unique TF (UTF)
Median unique TF length
% redundant TF PERL
% redundant TF CD-HIT-EST
Selecting bona fide transfrag and their functional annotation
Classification and annotation of the non-redundant N. crassa transfrags
№ of UTF
№ of orphan UTF
№ of bona fide UTF
№ of orphan UTF with blast hit
№ of bona fide UTF with blast hit
№ of bona fide UTF with MultiPfam2go
Non-redundant assemblies were annotated using BLAST and MultiPfam2go (Table 3). We note that in all assemblies only a small proportion of orphan transfrags had a BLAST match. Despite the highest number of orphan transfrags, Trinity had the least number of BLAST hits to transfrags in this category. In contrast, at least 70% of bona fide transfrags from all assemblies had a BLAST hit. This represented over 94% of cumulative BLASTx retrievable hit of the unfiltered assembly (Additional file 1). This number is higher than the ones typically reported in studies on de novo assembled transcriptomes [12,39]. In addition, bona fide transfrags were annotated with MultiPfam2go. The fraction of transfrags that could be associated with gene ontology terms ranged from 33%-50%, which is also high for domain based annotation .
Assessing transfrag integrity and gene coverage
Summary of bona fide † and orphan* transfrags integrity and validity
№ of bona fide UTF
№ of chimeras in unmapped transfrags ζ
Alignment of TF to reference genes (CDS)
Alignment of TF to reference genome
№ of TF Cov 50%, ID 50%
№ of reference unigenes
№ of TF Cov 90%, ID 90%
№ of reference unigenes
№ of TF uniquely mapped
№ of N. crassa genes identified by TF
Selecting bona fide assembly-derived transcripts in other species
Allocation of BLASTX hits between bona fide and orphan transfrags inferred with IFRAT
№ of TF in publication
№ of TF with hit in publication
№ of UTF > = 100
№ of orphan UTF
№ of orphan UTF with blast hit
№ of bona fide UTF
№ of bona fide with blast hit
Single k-mer assemblies of transcriptomes are considered incomplete because a short k-mer result in a highly diverse but also fragmented and redundant assembly, while a long k-mer provides a more contiguous assembly but misses poorly expressed transcripts . To account for this problem the multiple k-mer transcriptome assembly approach was introduced [28,41]. However, the number of sequences generated in this way exceeds by far the number of protein coding genes likely to exist in the respective organism , making identification of genuine transfrags a major challenge for downstream analysis. To reduce redundancy, clustering or merging methods are currently being applied [4,27,43]. Yet, these methods rely on common word heuristics, ignoring the biological nature of assembled transcripts . Therefore, reference-free clustering tends to mis-assign transfrags to biologically unrelated clusters  which leads to loss of unique functional annotations  and creation of chimeric transcripts .
Here, we propose IFRAT, a workflow that allows selection of unique bona fide transfrags (with CDS and coding potential) without clustering; and introduce domain co-occurrence analysis as means of tranfsrag assembly verification. IFRAT filters unique transfrags by removing exact duplicates, including identical forward and reverse nucleotide subsequences. IFRAT filtering removes slightly more transfrags than CD-HIT-EST at 100% identity because this program does not properly process transfrags containing N’s (author’s personal communication). Our results suggest that single k-mer assemblies may not need this filtering step since the proportion of redundant transfrags in the Trinity and Oases-25 datasets were only about 1%. In contrast, redundancy filtering is particularly important in multiple k-mer assemblies, considering that nearly half the transfrags in the Oases-M and Oases-P datasets were exact copies or substrings of other transfrags. It is unknown at what percent identity clustering results in significant loss of unique functional annotations. However, as suggested by our analysis, clustering without biological insight should be handled with caution because at 99% identity a significant subset of potentially unique transfrags is removed by CD-HIT-EST.
After filtering, IFRAT classifies the sequences into bona fide and orphan transfrags based on CDS prediction and coding potential. Our subsequent BLAST analysis corroborated this categorization, since 70-80% of bona fide transfrags had significant BLAST matches while this was true for only 1-6% of orphan tranfrags. We note that the median length of bona fide transfrags ranged from 1.5kb (Trinity) to 2kb (Oases), which is consistent with the average coding sequence length in fungi  while most of the orphan transfrags were short (med. 147-198 bp). However, our results confirmed previous findings that length is not the only indicator of coding potential  and ‘non-blastable’ transfrags , since 6-26% of the orphan transfrags with BLAST matches were less than 200 bp long.
All four assembly methods produced high quality datasets, as 76-90% of the transfrags mapped to the genome and the predicted CDS of N. crassa at high identity and coverage. Bona fide transfrags represented approximately 73% of the 10,785 known gene loci in the N. crassa genome. In addition, they indicated the existence of 715-1168 unknown potentially coding gene locations. Orphan transfrags also mapped to known gene locations, but most of these locations were represented by longer bona fide transfrags. These orphan transfrags may represent biologically interesting data, such as truncated assemblies (e.g. rare exons, poorly expressed genes, transcript with under-sampled regions), or immature mRNA with intronic regions and long UTRs for which coding potential could not be predicted [20,48,49]. Orphan transfrags that mapped to non-coding regions of the genomes represented ribosomal and non-coding RNA , and may be of interest. In any case, it is advisable to verify the correct assembly of orphan transfrags and remove mis-assemblies using a suitable reference dataset, such as a reference genome or EST collection. We integrated multi-domain co-occurrence architecture  to complement BLAST annotation. This avoids non-specific annotation of promiscuous domains resulting from truncated transfrags. Between 44% and 50% of the bona fide tranfrag peptides from N. crassa were assigned at least one GO term. Using IFRAT, we improved annotation coverage of published transcriptome datasets from non-model organisms. The choice of database and to a larger extend the coverage filter threshold accounts for small differences in the number of BLAST hits between bona fide transfrags and unfiltered assemblies. We attribute this high annotation coverage to the error tolerant CDS prediction  and selection of longer proteins with coding potential by IFRAT.
IFRAT is able to select bona fide transfrags irrespective of the assembler or assembly method used. Profound differences between transcriptome assemblers and assembly methods have been elaborately dealt with elsewhere [4,41,51,52]. We note however that Trinity performed very similar to Oases-P in identifying CDS and known gene loci, requiring substantially less computational resources. Other technical limitations, such as runtime and data-size, may influence the choice of one assembler over the other . Since many more transfrags were produced by the multiple k-mer assemblies that identified a comparable number of gene loci, they may be suitable for studies with interest on splice variants.
We have proposed a framework for post-assembly analysis of transcriptome assembly that is flexible enough to accommodate sequencing error and frame-shifts and that does not rely on the availability of a reference genome. Through this, a catalogue of reliable protein coding transfrags is established that represents a reference transcriptome. The method described herein is potentially applicable not only to assemblies of transcribed fragments generated with Trinity or Oases but also to assemblies produced by de Bruijn graph assemblers where no reliable sequenced genome is available, as demonstrated with the published datasets. Our framework performs well in segregating functionally relevant transcripts. We note that the main challenge remains the quality of assembly-derived-transcripts, which is undermined by incorporation of non-coding fragments that reduces the coding potential signal. One possible avenue for improvement is to screen RNA-seq reads for non-coding transcribed fragments prior to de novo assembly.
This work was supported by the South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation of South Africa. We are thankful to Dr. Kristoffer Forslund from the European Molecular Biology Laboratory, Germany with assistance in implementing MultiPfam2go, Mr Nicholas Thorne from the Advanced Computer Engineering Laboratory Group at the Centre for High Performance Computing, Cape Town, South Africa with discussions around implementing accelerated BLAST and Dr. Ana Riesgo at the University of Barcelona for making available the transcriptome assemblies of A. grandidieri, C. marginatus, C. olivaceus, C. elegans, and H. samnitica.
- Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011;12:671–82.View ArticlePubMedGoogle Scholar
- Pettersson E, Lundeberg J, Ahmadian A. Generations of sequencing technologies. Genomics. 2009;93:105–11.View ArticlePubMedGoogle Scholar
- Mbandi SK, Hesse U, Rees DJG, Christoffels AG. A glance at quality score: implication for de novo transcriptome reconstruction of Illumina reads. Bioinforma Comput Biol. 2014;5:17.Google Scholar
- Duan J, Xia C, Zhao G, Jia J, Kong X. Optimizing de novo common wheat transcriptome assembly using short-read RNA-Seq data. BMC Genomics. 2012;13:392.View ArticlePubMedPubMed CentralGoogle Scholar
- Sharov AA, Dudekula DB, Ko MSH. Genome-wide assembly and analysis of alternative transcripts in mouse. Genome Res. 2005;15:748–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Haznedaroglu BZ, Reeves D, Rismani-Yazdi H, Peccia J. Optimization of de novo transcriptome assembly from high-throughput short read sequencing data improves functional annotation for non-model organisms. BMC Bioinformatics. 2012;13:170.View ArticlePubMedPubMed CentralGoogle Scholar
- Jones CE, Baumann U, Brown AL. Automated methods of predicting the function of biological sequences using GO and BLAST. BMC Bioinformatics. 2005;6:272.View ArticlePubMedPubMed CentralGoogle Scholar
- Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, et al. InterProScan: protein domains identifier. Nucleic Acids Res. 2005;33(Web Server issue):W116–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005;21:3674–6.View ArticlePubMedGoogle Scholar
- Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420–35.View ArticlePubMedPubMed CentralGoogle Scholar
- Miller HC, Biggs PJ, Voelckel C, Nelson NJ. De novo sequence assembly and characterisation of a partial transcriptome for an evolutionarily distinct reptile, the tuatara (Sphenodon punctatus). BMC Genomics. 2012;13:439.View ArticlePubMedPubMed CentralGoogle Scholar
- Sun C, Li Y, Wu Q, Luo H, Sun Y, Song J, et al. De novo sequencing and analysis of the American ginseng root transcriptome using a GS FLX Titanium platform to discover putative genes involved in ginsenoside biosynthesis. BMC Genomics. 2010;11:262.View ArticlePubMedPubMed CentralGoogle Scholar
- Koestler T, von Haeseler A, Ebersberger I. FACT: Functional annotation transfer between proteins with similar feature architectures. BMC Bioinformatics. 2010;11:417.View ArticlePubMedPubMed CentralGoogle Scholar
- Mount DW. Using the Basic Local Alignment Search Tool (BLAST). Cold Spring Harb Protoc. 2007;2007:pdb.top17.View ArticleGoogle Scholar
- Galperin MY, Walker DR, Koonin EV. Analogous Enzymes: Independent Inventions in Enzyme Evolution. Genome Res. 1998;8:779–90.PubMedGoogle Scholar
- Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, et al. A large-scale evaluation of computational protein function prediction. Nat Methods. 2013;10:221–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Vogel C, Berzuini C, Bashton M, Gough J, Teichmann SA. Supra-domains: Evolutionary Units Larger than Single Protein Domains. J Mol Biol. 2004;336:809–23.View ArticlePubMedGoogle Scholar
- Galagan JE, Calvo SE, Borkovich KA, Selker EU, Read ND, Jaffe D, et al. The genome sequence of the filamentous fungus Neurospora crassa. Nature. 2003;422:859–68.View ArticlePubMedGoogle Scholar
- Smedley D, Haider S, Ballester B, Holland R, London D, Thorisson G, et al. BioMart – biological queries made easy. BMC Genomics. 2009;10:22.View ArticlePubMedPubMed CentralGoogle Scholar
- Logacheva MD, Kasianov AS, Vinogradov DV, Samigullin TH, Gelfand MS, Makeev VJ, et al. De novo sequencing and characterization of floral transcriptome in two species of buckwheat (Fagopyrum). BMC Genomics. 2011;12:30.View ArticlePubMedPubMed CentralGoogle Scholar
- Wenger Y, Galliot B. RNAseq versus genome-predicted transcriptomes: a large population of novel transcripts identified in an Illumina-454 Hydra transcriptome. BMC Genomics. 2013;14:204.View ArticlePubMedPubMed CentralGoogle Scholar
- Feldmeyer B, Wheat CW, Krezdorn N, Rotter B, Pfenninger M. Short read Illumina data for the de novo assembly of a non-model snail species transcriptome (Radix balthica, Basommatophora, Pulmonata), and a comparison of assembler performance. BMC Genomics. 2011;12:317.View ArticlePubMedPubMed CentralGoogle Scholar
- Riesgo A, Andrade SCS, Sharma PP, Novo M, Pérez-Porro AR, Vahtera V, et al. Comparative description of ten transcriptomes of newly sequenced invertebrates and efficiency estimation of genomic sampling in non-model taxa. Front Zool. 2012;9:33.View ArticlePubMedPubMed CentralGoogle Scholar
- Smeds L, Künstner A. ConDeTri - A Content Dependent Read Trimmer for Illumina Data. PLoS ONE. 2011;6:e26314.View ArticlePubMedPubMed CentralGoogle Scholar
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29:644–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Zerbino DR, Birney E. Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Schulz MH, Zerbino DR, Vingron M, Birney E. Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012;28:1086–92.View ArticlePubMedPubMed CentralGoogle Scholar
- Surget-Groba Y, Montoya-Burgos JI. Optimization of de novo transcriptome assembly from next-generation sequencing data. Genome Res. 2010;20:1432–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.View ArticlePubMedGoogle Scholar
- Arrial RT, Togawa RC, Brigido MM. Screening non-coding RNAs in transcriptomes from neglected species using PORTRAIT: case study of the pathogenic fungus Paracoccidioides brasiliensis. BMC Bioinformatics. 2009;10:239.View ArticlePubMedPubMed CentralGoogle Scholar
- Shimizu K, Adachi J, Muraoka Y. Angle: A Sequencing errors resistant program for predicting protein coding regions in unfinished cDNA. J Bioinform Comput Biol. 2006;04:649–64.View ArticleGoogle Scholar
- Eddy SR. Accelerated Profile HMM Searches. PLoS Comput Biol. 2011;7:e1002195.View ArticlePubMedPubMed CentralGoogle Scholar
- Forslund K, Sonnhammer ELL. Predicting protein function from domain content. Bioinformatics. 2008;24:1681–7.View ArticlePubMedGoogle Scholar
- Kent WJ. BLAT—The BLAST-Like Alignment Tool. Genome Res. 2002;12:656–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Wu TD, Watanabe CK. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005;21:1859–75.View ArticlePubMedGoogle Scholar
- Stabenau A, McVicker G, Melsopp C, Proctor G, Clamp M, Birney E. The Ensembl Core Software Libraries. Genome Res. 2004;14:929–33.View ArticlePubMedPubMed CentralGoogle Scholar
- Kupfer DM, Drabenstot SD, Buchanan KL, Lai H, Zhu H, Dyer DW, et al. Introns and Splicing Elements of Five Diverse Fungi. Eukaryot Cell. 2004;3:1088–100.View ArticlePubMedPubMed CentralGoogle Scholar
- Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Franchini P, van der Merwe M, Roodt-Wilding R. Transcriptome characterization of the South African abalone Haliotis midae using sequencing-by-synthesis. BMC Res Notes. 2011;4:59.View ArticlePubMedPubMed CentralGoogle Scholar
- Gibbons JG, Janson EM, Hittinger CT, Johnston M, Abbot P, Rokas A. Benchmarking Next-Generation Transcriptome Sequencing for Functional and Evolutionary Genomics. Mol Biol Evol. 2009;26:2731–44.View ArticlePubMedGoogle Scholar
- Martin J, Bruno VM, Fang Z, Meng X, Blow M, Zhang T, et al. Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads. BMC Genomics. 2010;11:663.View ArticlePubMedPubMed CentralGoogle Scholar
- Cahais V, Gayral P, Tsagkogeorga G, Melo-Ferreira J, Ballenghien M, Weinert L, et al. Reference-free transcriptome assembly in non-model animals from next-generation sequencing data. Mol Ecol Resour. 2012;12:834–45.View ArticlePubMedGoogle Scholar
- Kenny NJ, Shimeld SM. Additive multiple k-mer transcriptome of the keelworm Pomatoceros lamarckii (Annelida; Serpulidae) reveals annelid trochophore transcription factor cassette. Dev Genes Evol. 2012;222:325–39.View ArticlePubMedGoogle Scholar
- Hazelhurst S, Hide W, Lipták Z, Nogueira R, Starfield R. An overview of the wcd EST clustering tool. Bioinformatics. 2008;24:1542–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Tang Z, Choi J-H, Hemmerich C, Sarangi A, Colbourne JK, Dong Q. ESTPiper – a web-based analysis pipeline for expressed sequence tags. BMC Genomics. 2009;10:174.View ArticlePubMedPubMed CentralGoogle Scholar
- Galagan JE, Henn MR, Ma L-J, Cuomo CA, Birren B. Genomics of the fungal kingdom: Insights into eukaryotic biology. Genome Res. 2005;15:1620–31.View ArticlePubMedGoogle Scholar
- Frith MC, Bailey TL, Kasukawa T, Mignone F, Kummerfeld SK, Madera M, et al. Discrimination of non-protein-coding transcripts from protein-coding mRNA. RNA Biol. 2006;3:40–8.View ArticlePubMedGoogle Scholar
- Cui P, Lin Q, Ding F, Xin C, Gong W, Zhang L, et al. A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing. Genomics. 2010;96:259–65.View ArticlePubMedGoogle Scholar
- Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Meth. 2011;8:469–77.View ArticleGoogle Scholar
- O’Neil D, Glowatz H, Schlumpberger M. Ribosomal RNA Depletion for Efficient Use of RNA-Seq Capacity. In: Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG, Smith JA, Struhl K, editors. Curr Protoc Mol Biol. Hoboken, NJ, USA: John Wiley & Sons, Inc; 2013.Google Scholar
- Clarke K, Yang Y, Marsh R, Xie L, Zhang KK. Comparative analysis of de novo transcriptome assembly. Sci China Life Sci. 2013;56:156–62.View ArticlePubMedGoogle Scholar
- Lu B, Zeng Z, Shi T. Comparative study of de novo assembly and genome-guided assembly strategies for transcriptome reconstruction based on RNA-Seq. Sci China Life Sci. 2013;56:143–55.View ArticlePubMedGoogle Scholar
- Zhao Q-Y, Wang Y, Kong Y-M, Luo D, Li X, Hao P. Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinformatics. 2011;12 Suppl 14:S2.View ArticleGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.