YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut [version 2; peer review: 3 approved]

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software. Chakraborty et al. implemented a workflow for error estimation and correction, functional annotation and abundance estimation in RNA-seq data. They explored a methodology of analyzing longest ORFs of transcripts, using BLAST, as means to identify important genes. Although BLAST has been very commonly used for annotation, the authors proposed a very systematic approach of dividing the annotations into four sets based on the quality of the ORFs and their functional assignments. and coworkers proposed a new platform for analysing transcriptomic data from RNA-seq (YeATS -Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). The key feature of the tool highlighted by the authors is error estimation and correction of assembled transcripts, which is performed by analysing ORFs predicted in each transcript and merging of transcripts. This error-filtering step is supposedly missing in most other existing tools to date. In addition, YeATS is able to perform other common RNA-seq analytic tasks, such as transcript abundance estimation.


Introduction
Analysis of the complete set of RNA molecules in a cell, the transcriptome, is critical to understanding the functional aspects of the genome of an organism. Most transcripts get translated into proteins by the ribosome 1 . Non-translated transcripts (noncoding RNAs) may be alternatively spliced and/or broken into smaller RNAs, the importance of which have only recently been recognized 2 . Transcriptional levels vary significantly based on environmental cues 3 , and/or disease 4 . Quantifying transcriptional levels constitutes an important methodology in current biological research. Traditional methods like RNA:DNA hybridization 5 and short sequence-based approaches 6 have been supplanted recently by a high-throughput DNA sequencing method -RNA-seq 7,8 . Concomitant with the introduction of RNA-seq has been the development of a diverse set of computational methods for analyzing the resultant data 9-21 .
In the current work, we present a methodology for analyzing RNA-seq data that has been assembled into transcripts (YeATS -Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). The process of associating genomic open reading frames (ORF) to a set of transcripts (transcriptome) is the key step in YeATS, enabling identification and correction of specific errors arising from sequencing and/or assembly, a novel feature missing in most known tools. These errors include transcripts that have not been merged, a transcript having broken ORFs and transcripts containing long repeats. Also, YeATS identifies noncoding RNAs by comparison to compiled databases 22 , transcripts with multiple coding sequences and highly transcribed genes (based on simple normalization of raw counts followed by sorting).
Here, the YeATS workflow is demonstrated using a representative sample of the transcriptome from the tissue at the heartwood/ sapwood transition zone in black walnut (Juglans nigra L.). We have identified transcripts that have sequencing and/or assembly errors (~5%). A novel feature that emerged from our analysis was the presence of a highly transcribed gene that had no known homologous counterpart in the entire BLAST database. The amino acid composition of the longest open reading frame of this gene consists of a high percentage of leucine, histidine and valine, and classifies this as a putative extensin 23 . Given the economic and ecological importance of black walnut timber, characterization of such genes will enhance our understanding of the mechanisms underlying the unique properties associated with the wood of these trees 24 . The significance of proline-rich proteins 25 , dehydrins 26 , senescenceassociated proteins 27 and DNAJ 28 proteins to the formation of heartwood was established through their transcriptional abundance.
Finally, based on transcripts that have no known homologs, we have identified noncoding RNAs by comparison with the noncoding RNA database for Arabidopsis 22 . Thus, in the current work, we present a workflow (YeATS) with several novel features absent in most currently available software.

In silico methods
The input to YeATS is a set of post assembly transcripts as a fasta file (φ TRS ). The first step is to identify the set of genes (proteins) encoded by φ TRS . This is done by associating a proper open reading frame (ORF) to each transcript. This involves a comprehensive automated BLAST run 29 .
For each transcript in φ TRS , we generate the three longest ORFs (using the 'getorf' utility in the EMBOSS suite 30 ) ( Figure 1). These three ORFs are BLAST'ed to the full non-redundant protein sequences ('nr') database. For a given E-value cutoff (1E-12 in the current work), we create four sets 1. Only one ORF is less than the cutoff -the transcript is uniquely annotated.
2. None of the ORFs is less than the cutoff -the transcript has no known homologs.

Amendments from Version 1
In this version, we have 1) Added two new authors based on their inputs to the manuscript 2) Provided IDs to the submissions of the transcriptome(s).
3) Created github repository with README. It is to be noted that this is not meant to be a software article, so the software provided is not release quality. https://github.com/sanchak/YEATSCODE1 4) Incorporated several minor points raised by reviewer.

REVISED
3. More than one ORF is less than the cutoff.
(a) The ORFs map to different fragments of the same protein.
This points to an error in the sequencing or the assembly, which breaks down the contiguous ORF into two fragments.
(b) The ORFs map to different proteins -these are instances of a transcript having two valid ORFs. We duplicate the transcript, associating each one to a different protein sequence.
To produce the uniquely annotated set of genes, we ignored entries with the keywords chromosome, hypothetical, unnamed, unknown and uncharacterized, in order to have a functional characteristic in the annotation, provided the final annotated entry has low E-value. Also, apart from comparing E-values, we also compare the BLAST score, choosing an ORF as unique if its BLAST score was more than twice any other BLAST score, even if other scores satisfied the E-value criteria.
Algorithm 1 describes the process of merging transcripts (SI Figure 1). For a given length (which varies from 5 to 15 in this case), the 5' and 3' sequences and identifiers of each transcript are stored in new string databases: 3'=Begin; 5'=End. Repetitive strings (strings that have only two letters) are ignored, as it is difficult to ensure their uniqueness. For each string of n length in the Begin (3') string database, we find whether: a) unique matches of ] end end end /* This is not a TRS, but an input -remove this from the set*/ remove G from φ genes ; return φ genes ; end n length (one-to-one mapping) are present in the End (5') string database and b) that the prefixes (initial transcript identifiers) of the transcripts are the same.
Algorithm 2 describes the iterative method for identifying homologous genes in the genome based on the transcriptome. First, the transcriptome is converted to a set of protein sequences by choosing the appropriate ORF (described above) as the representative protein sequence, and a BLAST database (TRSDB) is created. An input protein sequence (possibly from another organism) of a gene of interest is used to query TRSDB using BLAST 29 . This results in a set of significant transcript matches which is pruned based on a cutoff identity (40% in this case) and the criterion that the sequence length should not differ more than another parameterizable value (50 in this case). Both these transcripts are now potential genes, and the above mentioned process is repeated for each of them, until no new transcripts are added.
The raw counts for each transcript is normalized according to Equation 1, assuming a read length of 100.
The runtimes for most of the processing required in YeATS is a few hours on a simple 16 GB, 16-core machine, barring the search for homologies in the BLAST 'nr' database. This search can be significantly accelerated when the organism under investigation has well-annotated protein databases (as in the current case), much in lines of the newly introduced SMARTBLAST (http://blast.ncbi. nlm.nih.gov/smartblast/), to runtimes under a day.

In vitro methods
Total RNA was isolated from the xylem region immediately external to the heartwood of a 16 year-old black walnut. The tree was felled in November, cross sections about 1 inch thick were taken from the base and dropped immediately into liquid nitrogen. After the sections were fully frozen they were transported to the lab on dry ice. The transition zone was then chiseled and the xylem was ground using a freezer mill. The RNA was extracted from 100g of ground wood using lithium chloride extraction buffer, and subsequently treated with DNAse (to remove genomic DNA) using an RNA/ DNA Mini Kit (Qiagen, Valencia, CA) per the manufacturers protocol. Presence of RNA was confirmed by running an aliquot on an Experion Automated Electrophoresis System (Bio-Rad Laboratories, Hercules, CA).
The cDNA libraries were constructed following the Illumina mRNA-sequencing sample preparation protocol (Illumina Inc., San Diego, CA). Final elution was performed with 16 μL RNase-free water. Each library was run as an independent lane on a Genome Analyzer II (Illumina, San Diego, CA) to generate paired-end sequences of 85bp in length from each cDNA library.
Prior to assembly, all reads underwent quality control for pairedend reads and trimming using Sickle 33 . The minimum read length was 45bp with a minimum Sanger quality score of 35. The quality controlled reads of 19 libraries from J. regia were de novo assembled with Trinity v2.0.6 14 (standard parameters with minimum contig length of 300bp) (manuscript in submission, bioproject id PRJNA232394). Subsequently, the reads from the TZ from J. nigra was aligned to this transcriptome and counts obtained by BWA's short read aligner v.0.6.2 ('bwa aln') (http://bio-bwa.sourceforge. net/) 34 . The Illumina reads for the transition wood transcriptome can be accessed at http://www.ncbi.nlm.nih.gov/sra/SRX404331.

Results
The input dataset to the YeATS tool was a set of transcripts, transcript identifiers and their corresponding raw counts (see Supporting information), obtained from the tissue at the heartwood/sapwood transition zone (TZ) in black walnut (Juglans nigra L.) ( Figure 2). These raw counts were normalized (see Methods), and transcripts with zero counts were ignored (see rawcounts.normalized.TZ in Dataset 1). There were ~24K such transcripts ( ) In order to associate a transcript to a specific open reading frame (ORF), the ORFs of TZ transcript φ is obtained using 'getorf' from the Emboss suite 30 (see ORFS.tgz in Supporting information) ( Figure 1). The three longest ORFs for each transcript is BLAST'ed to the full non-redundant protein sequences ('nr') database, and the results were used to characterize the genes.
There were ~1200 transcripts that had possible sequencing or assembly errors, ~22K transcripts that had significant matches (E-value<E-12) in the 'nr' database, 113 transcripts that had lower matches (E-12<E-value<E-08) in the 'nr' database, ~700 transcripts that had no matches in the 'nr' database and about 200 transcripts that could be merged based on overlapping amino acid sequences. We describe these in detail below.
Possible sequencing error or mis-assembly of transcripts We observed transcripts that had multiple ORFs that matched to the same gene with high significance (E-value<E-10). The possibility that such an occurrence is not an experimental artifact is low. Transcript C15259_G1_I1 is one such example, having two ORFs -ORF_36 (length = 144) and ORF_9 (length = 122), both of which match to the mitochondrial ATP-dependent Clp protease proteolytic subunit 2 35 (GenBank: CAN64666.1) from Vitis vinifera with E-values of 6E-92 and 7E-45, respectively. Figure 3 shows the alignment of these two ORFs to the Vitis vinifera protein indicated the possible site of the sequencing error or transcript misassembly. This aspect of the YeATS methodology can be used to estimate the sequencing and transcript assembly error rate. For example, in the current transcriptome of the walnut TZ, we found a 5% (1200 out of 24,000) error rate.
Long repeat within the same transcript A small number of transcripts had long repeats (on the reverse strand), as identified by transcripts that had multiple identical ORFs. For example, transcript C50369_G5_I2 has two ORFs (length = 143) that matched to an uncharacterized protein (Uniprot id: XP_009362671, E-value= 4e-13). These ORFs were located on the reverse strand, and were exactly the same (Figure 4). There were only 8 such cases.

Merging transcripts
About ~200 transcripts have been merged using conservative metrics by YeATS (see Methods, list.merge in Supporting information). For example, transcripts C55368_G1_I3 and C55368_G2_I1 were merged based on a stretch of 12 amino acids (NFDENRGALNSH) ( Figure 5). The indicated single nucleotide difference might be the reason for the failure of the assembly program to merge these two   The partial nucleotide sequences of these transcripts shows the repeat with only a single nucleotide difference. The indicated single nucleotide difference may explain the failure of the assembly program to merge these two transcripts. Interestingly, the transcript C55368_G1_I3 had two exact repeats of this stretch at the end which may have contributed to the failure of the assembly program to merge these transcripts. transcripts. Transcript C55368_G1_I3 had two exact repeats of this stretch, which is a likely assembly error.
Single transcripts with two ORFs Some transcripts were associated with multiple ORFs with distinct significant matches in the 'nr' database. We demonstrate this for the transcript C8909_G1_I1, which had two ORFs -ORF_104 (length = 331) and ORF_45 (length = 390) which matched to a clathrin light chain 36 (Uniprot id:XP_006481016.1, E-value=3E-126) and a leucine repeat rich receptor-like serine/threonine protein kinase 37 (Uniprot id: XP_007026739.1, E-value=0), respectively. These ORFs were on opposite strands, and did not overlap. It was not possible to ascertain which was the correct gene product, and it is a distinct possibility that both strands were transcribed 38 . A slightly different situation arose when both the ORFs were on the same strand 39 , as in the case of the transcript C54995_G6_I2. For example, in transcript C54995_G6_I2, there were two ORFs -ORF_157 (length = 464) and ORF_231 (length = 543) that matched to a RING/U-box superfamily protein 40 (Uniprot id: XP_007042454.1, E-value=7E-149) and a homeodomain-like superfamily protein isoform 41 (Uniprot id: XP_007030696.1, E-value=0), respectively. Both of these proteins were on the same (reverse) strand of the transcript. These transcripts are candidates for chimeric 42 or fusion 43 genes, since the ribosome is known to bypass small nucleotide stretches separating two ORFs 44 .
Highly transcribed genes Table 1 shows the transcripts with the highest counts. Interestingly, the most abundant transcript had no homologous counterpart in the full BLAST 'nr' or 'nt' database (GenBank accession: C52369_G2_I1). A proline-rich protein (PRP), a part of the protein superfamily of cell wall proteins consisting of extensins and nodulins, was found to have the second most abundant transcript 23,45 . Proline comprises 19% of the amino acids in the ORF of this transcript. PRPs are found as structural proteins in wood, and it was hypothesized that these proteins occur in the xylem cell walls during ligniflication, and influence the properties of wood 46 . PRPs were associated with carrot storage root formation 47 , were wound and auxin inducible 47 and implicated in cell elongation 48 . PRPs are also an integral component of saliva responsible for the precipitation of antinutritive and toxic polyphenols by forming complexes 49 . Two DNAJ/ HSP40 chaperone proteins, which are involved in proper protein folding, transport and stress response, showed high transcriptional levels 28 . Two DNAJ/HSP40 chaperone homologs (GenBank accession id: BI677935 and BI642398) were shown to be differentially expressed during summer at the sapwood/heartwood TZ of black locust 50 . The transcription levels of dehydrin-related proteins were shown to be seasonally regulated in the wood of deciduous trees 26,51 . However, this dehydrin protein is homologous to a 24kDa dehydrin (Uniprot id: AGC51777) from Jatropha manihot, a drought resistant plant 52 , unlike the ~100kDa proteins investigated in 26. Senescence-associated proteins, and the related tetraspanins, were also highly transcribed 27 . One highly expressed transcript was homologous to a protein that is yet to be characterized.

Finding genes
We demonstrated the (iterative) gene finding methodology in YeATS on a transcription factor that has an AP2 DNA binding motif (RAP2.6L in Arabidopsis, At5g13330) 53 . This protein showed differential tissue specific expression, and is likely to be involved in plant developmental processes and stress response 54 . Recently, the sequence of a homolog of RAP2.6L was deduced (Uniprot id: C1KH72, JnRap2) from an EST sequence isolated from tissue at the heartwood/sapwood TZ in black walnut (Juglans nigra L.), and its role in the integration of ethylene and jasmonate signals in the xylem and other tissues was established 55,56 . Using the sequence of JnRap2, we probed for other RAP2 genes in the TZ of walnut. We found three possible genes (C38523_G2_I1, C53728_G7_I1 and C53728_G7_I2) (Figure 7). It was observed that C53728_G7_I2  Table 1. A sample of highly transcribed genes with high normalized counts (NC). There are several highly transcribed genes in the representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone (TZ) in black walnut that did not have any significant homologs (NSL) in the complete 'nr' or 'nt' database. For the 'nr' database, we use the three longest ORFs as query. The significance of dehydrins, senescence-associated and DNAJ proteins can be observed through their transcription abundance. Transcripts with no significant matches in the 'nr' databasepossible long non-coding RNA genes?

ID
The top three ORFs of ~600 transcripts had no match in the BLAST 'nr' database. Although these may be unique genes, another possibility that must be considered is that these are non-coding RNA genes 2 . The nucleotide sequences of these 600 transcripts were BLAST'ed to the database of noncoding RNAs in Arabidopsis 22 . Three matches were identified: C52424_G5_I11, C52424_G5_I4 and C53565_G3_I1.
Both C52424_G5_I11 and C52424_G5_I4 are homologous to CR20, a cytokinin-repressed gene in excised cotyledons of cucumber, hypothesized to be non-coding RNA 57 . Analogous to the current work, the CR20 gene had alternate splicing 57 . C53565_G3_I1 had a 100% match to the Arabidopsis locus ATMG01380, a mitochondrial 5S ribosomal RNA, which is a component of the 50S large subunit of mitochondrial ribosome 58 .

Discussion
High-throughput mRNA sequencing (RNA-Seq) has revolutionized the field of transcript discovery, providing several advantages over traditional methods 7,8 . Following isolation and fragmentation of RNA and subsequent generation of cDNA libraries, a high-throughput sequencing platform is selected to generate short reads 59 . Reconstruction of transcripts from these short reads (assembly) may be performed using a reference genome or de novo algorithms 15-18,21,60 . Sequencing biases, variable coverage, sequencing errors, alternate splicing and repeat sequences are some of the challenges faced by these assemblers 14,61 .
Several post assembly computational tools provide further curation of transcripts resulting from the assemblers. The curation step involves identifying redundancies 19,20 , finding coding regions 62 , annotating the transcripts (https://transdecoder.github.io/) and detecting inaccuracies by aligning the transcripts to the genome 63 .
In the current work, we present an integrated workflow for RNAseq analysis (YeATS). YeATS includes most features of the tools mentioned above. Additionally, YeATS delivers several capabilities absent in these tools. A comprehensive BLAST analysis of the top three open reading frames of each transcript enables the identification of erroneous transcripts arising out of sequencing or assembly errors. These erroneous transcripts can be classified as: a) transcripts that have not been merged, b) transcripts that result in broken ORFs and c) transcripts that have long improbable repeats. Finally, YeATS provides annotation of the genes, enumerates homologous genes based on a template sequence and specified similarity threshold and identifies transcripts with multiple ORFs. The ribosome is known to bypass small nucleotide stretches separating two ORFs 44 . These are rare events, however, and thus unlikely to apply to the ~1200 transcripts that have broken ORFs pointing to the same gene 64 . Transcripts having multiple ORFs on the same strand are good candidates for chimeric 42 or fusion 43 genes dependent on ribosome bypassing.
The current work reveals and corroborates several aspects of the biology of hardwood trees. Probably, the most interesting is the detection of a highly transcribed gene (C52369_G2_I1) with no known homologs in the complete protein and nucleotide BLAST database, or significant matches in a database of long non-coding RNA genes 22 . If indeed the longest ORF of this transcript encodes a protein, it is 143 amino acids long, and is leucine (18%), histidine (13%) and valine (10%) rich ( Figure 8). Although it is likely that this is a protein with leucine rich repeats, these proteins are typically larger proteins 65 . On the other hand, histidine and valine rich extensins have been reported to be constituents of plant cell walls of dicots 23 . The regulatory stimuli of extensins are different for monocots (which also have different amino acid composition) and dicots 23 . A significant presence of extensin-like proteins in the cell wall of both developing and mature xylem (wood) have been reported for pine 46,66 . The publication of the walnut genome will aid the characterization of these genes by elucidating its promoter sequences.
Well characterized proteins like proline-rich proteins 25,46 , dehydrins 26 , senescence-associated proteins 27 and DNAJ/HSP40 chaperone 50 proteins were also abundant in the transcriptome. While Arabidopsis supports secondary growth, it fails to accumulate wood; it is therefore interesting to identify highly transcribed genes that are missing in the Arabidopsis proteome ( Table 2). The Figure 8. Percentage amino acid composition of the two most highly transcribed genes. C52369_G2_I1 has a high percentage of leucine, histidine and valine, and is a putative extensin. C51134_G2_I2 is proline and lysine rich, and is homologous to an extensin and nodulin.
DNAJ/HSP40 chaperone, dehydrins and tetraspanin proteins are found in the Arabidopsis proteome (TAIR10_pep_20101214 67 ), while the putative extensin, the proline-rich protein, a probable zinc transporter protein, an uncharacterized protein and senescence-associated protein appear to be unique to the walnut proteome.
Also, we corroborated the presence of a transcription factor that has a AP2 DNA binding motif 53,55 , and identify additional splice/ allelic variants with similar transcriptional levels. Once again, the knowledge of the walnut genome would enable a more profound understanding of such genes.

Conclusions
In summary, the current work elucidates an integrated workflow for RNA-seq analysis with several innovative features for identifying and correcting erroneously assembled transcripts. We demonstrated this workflow by characterizing the transcriptome of the tissue at the heartwood/sapwood TZ in black walnut. Software license GNU General Public License version 3.0 (GPLv3)

Author contributions
The YeATS tool suite was designed by Chakraborty, Britton and Wegrzyn did the analysis of the transcriptome, Woeste isolated the RNA, Butterfield was involved in the validation. The rest of the authors were involved in various aspects of the study design. Chakraborty wrote the first draft and the rest of the authors were involved in the editing.

Competing interests
No competing interests were disclosed.

Grant information
The authors wish to acknowledge support from the California Walnut Board and UC Discovery program.
I confirm that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Table 2. Identifying highly transcribed genes that are not present in the Arabidopsis proteome. The wood quality of walnut and Arabidopsis are quite different. It is informative to identify genes (proteins) that are absent in Arabidopsis, since they are likely to be responsible for the differences. The DNAJ/HSP40 chaperone, dehydrins and tetraspanin proteins are found in the Arabidopsis proteome, while the putative extensin, the proline-rich protein, a probable zinc transporter protein, an uncharacterized protein and senescence-associated protein appear to be unique to the walnut proteome.  Figure S1. Pictoral depiction of 'Begin' and 'End' database. The contents of the 'Begin' and 'End' databases are represented along with the shared amino acid residues at the 3' and 5' ends, respectively. The 'merge5' command identified the String1 pair in the 'Begin' and 'End' databases due to the shared sequence 'abcde'. The 'merge5' command failed to identify the String2 pair since six residues are shared. The 'merge6' command, however, will recognize String2 in the 'Begin' and 'End' databases, but would fail to recognize pairs that shared seven or more residues at the 3' and 5' 'End'. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Binay Panda
Genomics Applications and Informatics Technology laboratories (GANIT Labs), Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology(IBAB), Bangalore, Karnataka, India Chakraborty et al. implemented a workflow for error estimation and correction, functional annotation and abundance estimation in RNA-seq data. They explored a methodology of analyzing longest ORFs of transcripts, using BLAST, as means to identify important genes. Although BLAST has been very commonly used for annotation, the authors proposed a very systematic approach of dividing the annotations into four sets based on the quality of the ORFs and their functional assignments.

Overall comments:
The authors have done a good deal of work in exploring an important topic. The article gives many ideas worth exploring and directions for annotating data from de novo transcriptome sequencing. However, I suggest that the authors pay special attention to the usage of different terms (genome, transcriptome, RNA-seq, reads etc.) and be consistent in their usage throughout the manuscript. Additionally, the figure legends need complete re-writing and the perl scripts need to be included in the supporting information. Several explanations need to be provided throughout the manuscript (details below).
A single round of proofreading will hugely improve the manuscript.

Specific suggestions/questions:
The title states that YeATS identifies a highly transcribed putative extension. This is a bit misleading. YeATS takes as input already assembled transcripts from Trinity, estimates their expression, employs BLAST to try and assign a function to the most highly transcribed gene, and finds no known homologs. Only from the manual examination of the amino acid content of its longest ORF do the authors come to the conclusion that it is a putative extension. It will perhaps be better to mention the error-detection and estimation capabilities of YeATS as its strongest points.
TZ -please expand the abbreviation.
bwa aln gives aligned files, not counts. What was used to generate the raw counts? Also, bwa is not a splicing aware aligner. Authors should use Bowtie instead to do this, which may prove to be a better alternative.
All the figures need to be improved. In all figures, the sequence in question should be highlighted / boxed to make it easier for the reader to follow what is being talked about.
Figure 5 legend -'shows the repeat'. which repeat? Authors should clearly mention that there are 2 contiguous repeats of the same 39aa sequence in the transcript C55368_G1_I3. Also, there are 2 different reasons mentioned why the assembler could not merge the 2 transcripts in question. Please clarify which is the case.

GitHub repository
The README file requires substantial work. Some of the commands are quite confusing, comments are not clear, and perl scripts are not to be found. The numerous manual steps make the tool virtually un-useable. effort here will make the landing page much more appealing. Also the documentation on the Github page should provide detailed information on the expected inputs and outputs.
The colors in Figure 1 make the text a bit hard to read. As Figure 1 is often where many readers will go to understand what you are doing, you would benefit from making the colors lighter so the text is easier to read, and removing unnecessary shading, 3D effects, etc.

2.
Competing Interests: No competing interests were disclosed.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Varodom Charoensawan
Mahidol University, Bangkok, Thailand Chakraborty and coworkers proposed a new platform for analysing transcriptomic data from RNAseq (YeATS -Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). The key feature of the tool highlighted by the authors is error estimation and correction of assembled transcripts, which is performed by analysing ORFs predicted in each transcript and merging of transcripts. This error-filtering step is supposedly missing in most other existing tools to date. In addition, YeATS is able to perform other common RNA-seq analytic tasks, such as transcript abundance estimation.
From the point of view of a frequent user of NGS tools, rather than a developer, I can see that such a tool can be useful for improving transcript assembly and estimation, especially in organisms with no or poorly annotated genomes. However, there are a number of points that, to me, would improve the tool and the article, and it would be great if the authors could address/clarify. I would be happy to discuss this further if any of my comments are not clear. It would be nice to include a performance evaluation of this new platform against existing tools, or with vs' without the transcript error correction step by YeATS. One way to do this might be to take an existing RNA-seq dataset from a well-annotated organism such as Arabidopsis as a gold standard, and perform transcript assembly-estimation with and without correction by YeATS, and compare this to the transcript estimation using genomic information (e.g. by mapping reads to annotated transcriptomes/genomes). Does YeATS indeed improve the coverage and specificity of transcript estimation, for instance? ○ Along the same lines as the comment above, it would be useful if the authors could comment on the time and/or computing resources required to perform the correction step. Also, are the accuracy and computing resources dependent on the read lengths and/or sequencing platforms? (Or is it intended for Illumina reads as used in the example?).

○
Both the source code of YeATS and the data set used to illustrate its usage have been deposited and described at the end of the article. However, to this reviewer's understanding, there is a set of Perl scripts deposited to Github, but it is still not clear to me how the tool/workflow should be implemented. The README does not seem to describe this. Could the author point out if there is already a guideline or documentation on how to use or integrate YeATS into an existing NGS workflow, if that already exists? ○ To my understanding, the input of YeATS is a set of assembled transcripts performed by other tools (e.g. Trinity). However, this step was not clearly described in the "in vitro methods" section on Page 5. Instead, it seems the trimmed reads were directly aligned to J. ○ regia transcriptome, which is somewhat confusing. Could you please clarify these?
The authors described the genes as highly "transcribed" in walnut (according to RNA-seq from this study?) that are not present in Arabiodopsis "proteome". I found these to be slightly disconnected.

○
Minor comments: Figure 1: Is the "no" label between the boxes "Choose longest ORF" to "Gene annotation" necessary? I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Author Response 13 Oct 2015
Sandeep Chakraborty, University of California, Davis, USA We would like to thank you for taking the time to review this paper. Please find our responses below.
Chakraborty and coworkers proposed a new platform for analysing transcriptomic data from RNA-seq (YeATS -Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). The key feature of the tool highlighted by the authors is error estimation and correction of assembled transcripts, which is performed by analysing ORFs predicted in each transcript and merging of transcripts. This error-filtering step is supposedly missing in most other existing tools to date. In addition, YeATS is able to perform other common RNA-seq analytic tasks, such as transcript abundance estimation. From the point of view of a frequent user of NGS tools, rather than a developer, I can see that such a tool can be useful for improving transcript assembly and estimation, especially in organisms with no or poorly annotated genomes.
We appreciate your positive comments, and the possibility of value addition by the suggested methodology for existing NGS flows. We believe that this is the first attempt to associate the key information encoded by transcripts within ORFs to assess the accuracy of the assembly.
However, there are a number of points that, to me, would improve the tool and the article, and it would be great if the authors could address/clarify. I would be happy to discuss this further if any of my comments are not clear. It would be nice to include a performance evaluation of this new platform against existing tools, or with vs without the transcript error correction step by YeATS. One way to do this might be to take an existing RNA-seq dataset from a well-annotated organism such as Arabidopsis as a gold standard, and perform transcript assembly-estimation with and without correction by YeATS, and compare this to the transcript estimation using genomic information (e.g. by mapping reads to annotated transcriptomes/genomes). Does YeATS indeed improve the coverage and specificity of transcript estimation, for instance?
YeATS evaluates the accuracy of a transcriptome, but it is dependent on downstream tools (like MAKER) to use this for proper annotation of the genes. Thus, there are no existing tools that we could compare it with directly. A highly curated database like the Arabidopsis would not be a fair comparison, since it might have been annotated looking at several data points. However, we have extensively used the YeATS pipeline in processing the newly sequenced walnut genome (manuscript in review), and established erroneous assembly for several genes of interest. The transcriptome from several other tissues were included in the genome study. Interestingly, the 5% error estimate remained the same.
Along the same lines as the comment above, it would be useful if the authors could comment on the time and/or computing resources required to perform the correction step. Also, are the accuracy and computing resources dependent on the read lengths and/or sequencing platforms? (Or is it intended for Illumina reads as used in the example?).
The run times for most of the processing required in YeATS is a few hours on a 16 GB, 16core machine, barring the search for homologies in the BLAST 'nr' database, which can be time-intensive for a comprehensive search. This search can be significantly accelerated when the organism under investigation has well-annotated protein databases (as in the current case), much in lines of the newly introduced SMARTBLAST (http://blast.ncbi.nlm.nih.gov/smartblast/), to run times under a day. Run times are dependent on the number of transcripts only, since the input to YeATS is an assembled transcriptome from a tool like Trinity. We have included this information in the manuscript.
Both the source code of YeATS and the data set used to illustrate its usage have been deposited and described at the end of the article. However, to this reviewers understanding, there is a set of Perl scripts deposited to Github, but it is still not clear to me how the tool/workflow should be implemented. The README does not seem to describe this. Could the author point out if there is already a guideline or documentation on how to use or integrate YeATS into an existing NGS workflow, if that already exists?
We have provided a README that describes the step in the YeATS workflow. However, this is not a push-button methodology, and goes through several steps, each of which is dependent on the previous step. Also, we have used custom schedulers, and thus several steps need to be adjusted depending on available resources. For example, the number of parallel jobs and the time-step between each submission is controlled through a customscript. Thus, we have provided the key algorithms in detail in the paper for any developer to easily replicate our results. Furthermore, we are enhancing several of the programs based on more sophisticated algorithms (like using kmers, compression of data, etc). A proper release of this software will require some more time, but this manuscript was not meant to be a software article.
To my understanding, the input of YeATS is a set of assembled transcripts performed by other tools (e.g. Trinity). However, this step was not clearly described in the in vitro methods section on Page 5. Instead, it seems the trimmed reads were directly aligned to J. regia transcriptome, which is somewhat confusing. Could you please clarify these?
The input of YeATS is indeed a set of assembled transcripts performed by other tools like Trinity. We have modified the methods section to clarify this.
The authors described the genes as highly transcribed in walnut (according to RNAseq from this study?) that are not present in Arabiodopsis proteome. I found these to be slightly disconnected.
We agree that these results are slightly disconnected to the general narrative of this paper, which focuses on post-assembly methodologies to assess the accuracy of assembled transcripts. However, these are interesting results that emerge during the analysis of the transcriptome of the transition zone of walnut, which has been obtained for the first time.
And thus, though this may be of interest to researchers in the field, there is too little data to spin-off another paper to publish these findings.
Minor comments: Figure 1: Is the no label between the boxes Choose longest ORF to Gene annotation necessary?
We have changed the label 'no' to 'unannotated'. Long ORFs that do not have have significant matches are probably uncharacterized genes, and the genome could be annotated accordingly (although the annotation of novel genes is another problem not addressed in the current paper).
Page 3, 2nd column, line 12: modify the text to often in distinct regions of the transcript.. for clarity?
We have clarified this: 'The ORFs map to different fragments of the same protein. This points to an error in the sequencing or the assembly, which breaks down the contiguous ORF into two fragments.' Page 5, 1st column: There were 24K of such transcripts Figure 6s legend: These ORFs We have made these modifications. Once again, we are thankful for your insightful comments, and hope to have addressed your concerns.