dataesr/scientific-paragraphs-categorization huggingface dataset:
The other 17 genes that were found by Partek added new fragments to transcripts defined with Cufflinks. All told, using all four approaches, 61 out of the 63 "gold standard" genes were found (Figure  4C ). For each of the approaches, we characterized the transcripts and their translations. The median transcript length for the de novo fragments was 303 bp, with a median ORF length of 98 amino acids, and a median ORF coverage of 99%. The other methods resulted in longer sequences (median range 624-892 bp, 170-259 amino acids), but a lower ORF percent coverage, 89% for all arms (Additional file 2). The transcripts defined by the three primary approaches and by Partek, 219545 sequences, were clustered using TGICL (Figure  4B , Additional file 2). This process resulted in 36072 transcripts longer than 300 base pairs, including contigs and singletons as defined by TGICL.
Additional improvement of the transcriptome At this point, it was clear that TGICL lost transcripts during the clustering process. To overcome the problem of missing genes, the initial transcripts from the four approaches were clustered using CAP3 (Figure  4 ). Further redundancy was removed by CD-HIT-EST. The number of potential transcripts longer than 300 base pairs was 79939. In order to split artificially fused transcripts, the algorithm developed to split the sequences previously was used. 2393 sequences were split in two, and 66 were split in three. Once again, approximately half of the split sequences (1143) were from opposite strands. Redundancy at the protein level was removed with CD-HIT, and subsequently the redundant transcripts were removed, resulting in 79408 defined transcripts.
These transcripts were compared to the standard set, and 61 out of 63 transcripts were found. Two different clustering algorithms were applied to the collection of potential transcripts. TGICL strongly removed redundancy, and makes longer transcripts (median 1242 bp) but loses genes. CAP3 does not lose genes, but makes shorter transcripts (median 907 bp) and leaves redundancy in the collection (Additional file 2). To take advantage of both algorithms, the two collections were compared. Transcripts defined by CAP3 that were identical (19845) or were contained in (14953) TGICL defined transcripts were replaced by their matching counterparts in TGICL (30482). This reduced the number of transcripts by 4316. Additional transcripts defined by CAP3 that did not have a good match to TGICL defined ones (44610) were introduced into the final collection without changes.
The final collection included 75092 transcripts. The final collection was compared to the "gold standard" set both on the DNA and protein level. 61 transcripts and 47 proteins were found, an improvement versus the TGICL alone. The transcripts that were not detected on the protein level were analyzed in depth to discover the possible flaws in our pipeline. There were two major causes, one on the transcript level, and one in the translation process. On the transcript level, we discovered that many of the genes had both spliced forms and intronic read-throughs (intron retention) (Additional file 1: Figure  S9 , Additional file 6, Example 1). The retained introns disrupted the coding sequence. The second major cause of missing protein sequences was due to our definition of an open reading frame from stop-to-stop (Additional file 6, Example 2).
In addition, 269 junctions from 32 EST-based transcripts in 25 randomly chosen genomic loci were manually aligned and examined, and the percentage of GC junctions was 64% (Table  4 ). Splice junctions from automatically generated Cufflinks transcripts were also analyzed, and donor (73358) and acceptor (71535) sites were extracted from Emihu1plus. In order not to count the same junction twice in cases of alternate splicing, non-redundancy was performed on the genomic coordinates. The most prevalent splice donor is GC (61%, Table  3 , Figure  7C ) while the canonical sequence GT was only used 37% of the time. The splice acceptor is almost entirely the canonical AG (98. 5%). The even less canonical sequences (for example a donor not starting with a G), or at least part of them, represent misalignments of TopHat (Additional file 1: Figure  S3 ).
huxleyi. Others require a database of defined splice junctions, which is problematic to create due to the pervasive nature of the intronic reads (Additional file 1: Figure  S9 ). A manual inspection was performed on 535 introns taken from 68 loci, and 89% (478) showed reads throughout the length of the intron. The other instances of alternative splicing observed were: 8 exon skipping, 6 alternate acceptors, and 5 alternate donors.
Transcriptome annotations The final transcripts were annotated using Gene Ontology (GO)  [20]  terms by applying Blast2GO  [21]  at the DNA level. 61% (45704) of the transcripts had at least one Blast hit. GO terms were transferred to 84% (38510) of the transcripts with Blast hits, and finally, GO terms were successfully assigned to 40% of the total transcriptome (29868), after setting the Annotation Cut-Off to 45 (Additional file 1: Figure  S4 ). The total number of annotations was 143794; the mean number of GO annotations per gene was 5 and the mean level in GO was 5. 5 (Additional file 1: Figure  S5 ). The most frequent organism in the Blast top hits was Afipia broomeae with 7071 hits and the second one was Aureococcus anophagefferens with 2650 (Additional file 1: Figure  S6 ). A. Broomeae is a very well annotated species, and that is probably the reason for the large number of hits, as it is phylogenetically distant from E.
Assembly strategy One of the goals of this study was to achieve a complete picture of the transcriptome: utilizing all the available data, build accurate transcripts without either missing genes, or including spurious sequences. The data at hand was the genome assembly from JGI, ESTs from GenBank, and our RNAseq reads. The genome was incomplete, and the EST coverage was very partial, with only 129,000 sequences for a genome with an estimated 30,000 genes, and many more transcripts. The RNAseq data had good read depth, however, reads were relatively short (90 bp) and transcript coverage was incomplete due to the low complexity and highly repetitive nature of the underlying sequence. We used three different approaches to assemble the sequence: two utilized the reads, one genome based and one de novo, and an EST only branch.
For the readbased arms, the first decision that had to be made was whether to combine the reads from the different biological samples or to analyze them independently. For the genome-based approach, we chose to analyze each lane individually (Cufflinks), and then join them (Cuffcompare), while for the de novo assembly (CLC Assembly Cell), without the basis of the genome to build on, we used all the lanes together in order to maximize the chances of building a transcript, particularly those with low expression levels. The next decision was whether or not to include the ESTs in the de novo construction, as ESTs allow elongation of putative transcripts due to their length and different sequencing method (Sanger). We decided to cluster and analyze the ESTs on their own first, in order to improve the ESTs' reliability, and to combine the various tracks afterwards.
When the analysis was performed, the programs chosen were considered state-of-the-art. New tools have been developed in the interim, such as Trinity  [22]  and SOAPdenovo-trans  [23]  among others. Programs developed for genome assembly and later adapted to transcript assembly (SOAPdenovo-trans, Oases  [24] ) seem to be less accurate than purpose-built transcriptome tools (such as Cufflinks and Trinity, personal communication). While some of the newly developed tools may perform better building transcripts, a manually curated gene set is still highly recommended for troubleshooting, particularly for non-model organisms.
Lost in translation After we found all the "gold standard" transcripts possible in the dataset, we compared the hand curated and automated datasets on the protein level. Many genes were lost on the protein level, even though they could be detected on the DNA level. The first class of missing proteins was due to artificial joining of transcripts. This phenomenon has been observed in model as well as non-model plants  [1]. Transcripts were joined in several ways including on opposite strands overlapping in the 3' UTR, and on the same strand with some overlapping reads (possibly due to the repetitive nature of the genome). It is interesting to note that we did not observe any overlapping transcripts in the coding regions. We corrected many cases by using a script to split transcripts, based on non-overlapping open reading frames.
The "new" canon for E. huxleyi GC-AG, is 25% AT, and is much closer to the DNA at hand. The JGI "Best transcript" collection is composed of 68% ab initio predicted genes  [8]. Most of the ab initio algorithms are programed to look for canonical GT-AG junctions, and will only use non-canonical if there is nothing else. This leads to the many mispredicted junctions in that dataset. The non-canonical splicing may contribute to the high frequency of intron retention. This phenomenon was observed both in the manually curated as well as automatically processed transcripts. From the hundreds of alternate events inspected manually, mainly intron retention was observed. It is not clear if the transcripts with intron retention are functional, as in virtually all cases checked, it led to premature stop codons in the putative protein and disturbed the domain structure of the potential proteins (data not shown).
Comparison of programs Cufflinks missed putative exons and transcripts, where Partek Genomics Suite was able to define them. Partek describes reads mapped to the genome, while Cufflinks attempts to build gene structure. There was no obvious reason for the transcripts to be missed by Cufflinks, as these regions had a minimum of 50 reads and length of 300 bp. It may be that the high rate of intron retention did not allow Cufflinks to predict transcripts in those regions. We clustered the transcripts using both TGICL and Cap3. While TGICL utilizes Cap3, due to its initial Blast stage, it gives very different results. While TGICL reduced redundancy very efficiently, it lost many transcripts. On the other hand, Cap3 alone clustered well, but left redundancy in the dataset. In both cases, it is not clear why the programs did not perform optimally.
As there is no explanation of how or why sequences are removed by TGICL, we were surprised by this finding. Cap3 left the redundancy in the dataset due to the many alternative isoforms. The final dataset has redundancy, mainly due to putative splice variants. Due to the high level of intron retention, many transcripts are predicted both with and without introns. The programs cannot differentiate between real splicing and spurious splicing, though one possible way to discern correct transcripts would be to take intact reading frames into account. Parameters to measure quality of the transcriptome Using the manually curated transcripts, we found all of the transcripts possible given the constraints of minimum coverage per transcript. On the protein level, we found most of the genes, but have room to improve the pipeline.
Improved assembly of the Emihu1 genome The current assembly of the E. huxleyi genome: Emiliania_hux-leyi_CCMP1516v1. 0 (Emihu1) was downloaded from the JGI (Joint Genome Institute) website. In addition, reads that were not introduced in the assembly were obtained using the command wget http://genome. jgi-psf. org/Emihu1/download/Emihu1_unplaced_genomic_reads. fasta. gz. The unplaced reads were assembled using the Newbler runAssembly program, version 2. 3 (http://my454. com/products/analysis-software/index. asp) using default parameters. Newbler was chosen as it is the standard tool for 454 reads and therefore it also works for longer sequences like Sanger reads (which were used in the genome build). The resulting contigs were merged to the Emihu1 genome utilizing the Minimus2 software from the Amos package, version 3.
Transcriptome assembly and definition Sequence reads were deposited in GenBank SRA with the study accession number SRP017794. Sequence reads were trimmed to a length of 90 bp, and adaptors were removed using the cutadapt program  [26]. De novo assembly was performed with the CLC Assembly Cell (EMEA, Aarhus N, Denmark, http://www. clcbio. com/ products/clc-assembly-cell/) software, version 3. 2. 2. All the lanes were run together using default parameters. The genome based assembly was performed using the TopHat software, version 1. 3. 0 [14] , and was run for each sample separately using the Emihu1plus as the reference sequence and set to the default parameters, except for the minimum intron length that was set to a minimum of 30. Then Cufflinks (version 1. 1. 0) was run for each sample using the accepted_hits.
bam file as input and the -u -min-intronlength 30 options. Cuffcompare  [15]  was applied on the Cufflinks output of all the lanes together to define a list of transcripts that are comparable between all the samples. Transcript sequences were extracted using the Cuffcompare gtf file coordinates utilizing the Galaxy tool: Extract Genomic DNA (https://main. g2. bx. psu. edu/). Additional partial exons or transcripts missed by Cufflinks were extracted from the alignments using Partek® Genomics Suite™ software, version 6. 5, Partek Inc. , St. Louis, MO, USA. EST clustering was performed using TGICL version 2. 1 (http://compbio. dfci. harvard. edu/tgi/software/). Additional clustering of potential transcripts was performed with TGICL, CD-HIT-EST (version 4. 5. 4) [18]  or CAP3 (Version date: 10/15/07)  [12].
ORF extraction and transcript splitting were performed using in-house Perl scripts (available on request). The rules used for splitting were as follows: 1. Transcripts whose ORF covered at least 80% of the sequence were not split 2. Transcripts shorter than 2000 bp were not split, 3. Transcripts whose ORF covered between 10% and 80% of their sequence were split into 2 or 3 if the new ORFs did not overlap and the new transcript was at least 300 bp long. Transcripts whose ORF covered less than 10% of the sequence were removed from the data set. All sequence comparisons carried out for quality control or for any other purpose were performed by with Blat, version 34 × 12  [17].
Manual definition of genes The RefSeq (NCBI, http://www. ncbi. nlm. nih. gov/, Gene database) protein sequence of the target genes were taken from Homo Sapiens, Arabidopsis thaliana and Saccharomyces cerevisiae sequentially, starting from human, unless we had reason to believe that a different species might have a better chance of hits. The initial search was TBlastN  [16]  at JGI (http://genome. jgi-psf. org/pages/ blast. jsf?db=Emihu1) with the following parameters changed: Target database: Emiliania huxleyi v1 scaffolds (unmasked), 'Filter low complexity regions' off, and 'Perform gapped alignment' on. The hits from the various input species were compared to see if they hit the same genomic loci. Each locus was then inspected, starting with the hit with either the highest score or the longest region of similarity, to see if any transcript evidence was available -ESTs, as seen in the JGI browser, and after we sequenced the transcriptome, the reads mapped to IGV.
If there were ESTs available, they were assembled into transcripts using Sequencher version 5. 0 (GeneCodes Corporation, Ann Arbor Michigan), and compared to the predictions available at JGI (if there were any). If there was incomplete coverage, the putative transcript was improved using the JGI gene models and the Blast results. When the reads became available, the transcripts were corrected on the basis of the reads. If more than one genomic locus was found in the initial Blast, each successive hit was examined to see if it was truly an independent hit, representing a family member, or an artificial duplication, where the genomic or EST sequences was identical to a previously examined locus. If no ESTs (or reads) were available to use as an anchor for a predicted transcript, then a combination of the Blast hits and available predictions were used to construct a transcript.
Acknowledgements The authors would like to thank  Dr. Gilgi Friedlander  for providing scripts,  Ruth Khait  and  Adva Shemi  for help with the manual definition of genes. AV would like to acknowledge the support of: the  European Research Council (ERC) StG  ( INFOTROPHIC grant  # 280991 ), the  Israeli Science Foundation (ISF) Legacy Heritage fund  (grant # 1716/09 ),  International Reintegration Grant (IRG) Marie Curie  grant and the generous support of Edith and Nathan Goldenberg Career Development Chair.
Two sentence similarity matrices are computed: one between sentences in the development and training sets, and the other between sentences in the test and training datasets. 2. Each matrix entry m i j should contain the similarity score between the i t h sentence in the training set and the j t h sentence in the development (or test) set. 3. For each sentence s in the test and development sets, a phrase pair list L S of all potential phrases that can be used during decoding is extracted from the aligned training set. 4. The corresponding source-context similarity values are assigned to each phrase in lists L S according to valuesin thecorresponding similarity matrices.
Exper iments This section describes the experiments carried out in the context of WMT 2014. For English-Hindi the parallel training data was collected by Charles University and consisted of 3. 6M English words and 3. 97M Hindi words. There was a monolingual corpus for Hindi comming from different sources which consisted of 790. 8M Hindi words. In addition, there was a development corpus of news data translated specifically for the task which consisted of 10. 3m English words and 10. 1m Hindi words. For internal experimentation we built a test set extracted from the training set. We selected randomly 429 sentences from the training corpus which appeared only once, removed them from training and used them as internal test set. Monolingual Hindi corpus was used to build a larger language model.
The language model was computed doing an interpolation of the language model trained on the Hindi part of the bilingual corpus (3. 97M words) and the language model trained on the monolingual Hindi corpus (790. 8M words). Interpolation was optimised in the development set provided by the organizers. Both language models interpolated were 5-grams using Kneser-Ney smoothing. The preprocessing of the corpus was done with the standard tools from Moses. English was lowercased and tokenized. Hindi was tokenized with the simple tokenizer provided by the organizers. We cleaned the corpus using standard parameters (i. e. we keep sentences between 1 and 80 words of length). For training, we used the default Moses options, which include: the grow-diag-final and word alignment symmetrization, the lexicalized reordering, relative frequencies (conditional and posterior probabilities) with phrase discounting, lexical weights and phrase bonus for the translation model (with phrases up to length 10), a language model (see details below) and a word bonus model.
Acknowledgements This work has been supported in part by  Spanish Ministerio de Economía y Competitividad , contract  TEC2012-38939-C03-02  as well as from the  European Regional Development Fund (ERDF/FEDER). The work of the second and fourth authors is also supported by  WIQ-EI  (IRSES grant n. 269180) and DIANA-APPLICATIONS ( TIN2012-38603-C02-01 ) project and  VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.
Dataset The dataset used to generate the models comprised 12 625 distinct inhibitors (20 213 data points) assayed against 67 protein targets (20 213 datapoints, matrix B30% complete). ] [49]  The number of known compound activities per target is given in ST1 (ESI †) along with their UniProt and PDB IDs. 50 pChEMBL (Àlog 10(activity (nM))) values of five types of activity values were used (IC 50 , K i , AC 50 , EC 50 and K d ). The pChEMBL value (Fig. SF1 , ESI †) ranges from 3. 4 to 11. 7. According to this distribution, the dataset contains 15 375 active datapoints and 4821 inactive datapoints.
Protein descriptors Sequences were aligned using the Blosum62 matrix in Clustalw,  54, 55  whereas the superimposition and structural alignment of drug targets was performed using Chimera 1. 6. 56 Binding site residues were selected on the basis of a 3D structural alignment of the targets. As structural information was not available for some of the proteins, sequence alignment of the cavities in homologous proteins was used instead. Six amino acid (alignment dependent) descriptors, namely Z-scales, FASGAI, MS-WHIM, protein features, ST scales and T scales  15, 16  were calculated for the aligned binding site residues with the function AA_descs of the R package camb. 57 escriptor values for sequence alignment gaps were set to zero. The following full protein sequence descriptors (alignment independent descriptors) were calculated using Protpy:  19  amino acid composition (AAC), dipeptide composition (DPC), autocorrelation parameters, Moran autocorrelation (MA), normalised Moreau-Broto autocorrelation (MBA), sequence order coupling number (SOCN), Geary autocorrelation (GA), quasi sequence order (QSO) and composition, transition and distribution (CTD) descriptors.
Additionally, the Profeat 58 descriptor, which is a combination of all full protein sequence descriptors mentioned above, was computed. In order to investigate the predictive power of 3D target information,  59  the volume of the binding pocket of each target was calculated using trj_cavity. 60 Trj_cavity is a protein cavity analysis software, designed to characterise the cavities present in protein structures extracted from molecular dynamic (MD) trajectories. By using an optimised MD protocol, it identifies the binding pocket, whose coordinates are given by the user, and calculates the static cavity volume. The cavity volume was employed as a baseline to compare the predictive signal alignment dependent and independent descriptors.
Model training Data pre-processing. The complete data matrix was indexed by datapoints and columns by compound and target descriptors. Datapoints were centred to zero mean and scaled to unit variance, followed by removal of columns for which variance was near to zero with the function nearZeroVar from the R package camb  57  (frequency cut-off = 30/1). Model training was performed using random forest (RF) and were built with the R package caret. 61.
Feature analysis Each bit in the compound descriptors encodes the presence or absence of a chemical substructure, whereas the values of the Z-scales account for physicochemical properties of the amino acids. The influence of each compound substructure and amino acid property on bioactivity was evaluated in the following way. The value of the descriptor was set to zero in all compound or amino acid descriptors presenting it. Then, a PCM model was used to predict the bioactivity of compounds using the updated descriptors. This procedure was repeated for all compound and amino acid descriptors. The analysis was calculated on the basis of the final PCM model trained on all bioactivities, however, due to a lack of data in the training set, chance correlations could happen in the case of certain features of compounds.