dataesr/scientific-paragraphs-categorization huggingface dataset:
However, some discrepancies occurred according to the number and length/strength of hydrogen bonds anchoring the sugars to the carbohydrate-binding site. The docking experiments with OsEULS2 confirmed the previously reported substrate specificity for high-mannose N-glycans and lactosamine related structures  (Al Atalah et al. 2012 ). However, for OsEULD1A a substrate specificity for galactose related sugars or galactose containing glycoproteins was reported whereas mannose did not inhibit the agglutination of rabbit erythrocytes caused by OsEULD1A. Previous promoter analysis performed using the PLACE database (Al  Atalah et al. 2013 ) identified the presence of several TATA and CAAT boxes and identified three major classes of promoter elements, among which the light responsive elements, the ABA and GA responsive elements and the elements related to other (a)biotic stresses.
japonica genome (RGAP release 7) available from NCBI (https://blast. ncbi. nlm. nih. gov), MSU  (Kawahara et al. 2013)  and phytozome (https://phytozome. jgi. doe. gov), as described previously by  Van Holle and Van Damme (2015). Top hits were used for a consecutive BLAST search. In addition the MSU database  (Kawahara et al. 2013)  was searched using the Pfam domain identifier [ABA: PF07367 (fungal fruit body lectin); amaranthin: PF07468 (agglutinin domain); CRA: PF00704 (glycolhydro 18); cyanovirin: PF08881 (CVNH); EUL: PF14200 (ricin-lectin 2); GNA: PF01453 (B-lectin); hevein: PF00187 (chitin bind 1); JRL: PF01419 (jacalin); legume lectin: PF00139 (lectin legB); LysM: PF01476 (LysM domain); nictaba: PF14299 (PP2); ricin-B: PF00652 (ricin-B lectin)] of the different lectin domains. Protein sequences were downloaded from MSU  (Kawahara et al.
2013)  and screened for the presence of conserved protein domains using interproscan 5  (Mitchell et al. 2015). The program was downloaded (https://www. ebi. ac. uk/ interpro/download/) and locally installed. Indica lectins were identified by BLAST searches with the lectin domains of the japonica hits against the indica rice genome (ASM465v1) available from EnsemblPlants (http://plants. ensembl. org). As for the japonica sequences, these protein sequences were analyzed for the presence of conserved protein domains using Interproscan 5  (Mitchell et al. 2015). Only sequences with at least one lectin domain were retained. The protein sequences of the lectins were analyzed for the presence of signal peptides using SignalP 4. 1  (Petersen et al. 2011 ) and the presence of transmembrane domains was analyzed using TMHMM 2.
Mapping lectin sequences to chromosomal locations and analysis of duplication events The putative lectin genes were mapped on the different chromosomes using the MapChart software  (Voorrips 2002). The transcription start sites of the putative lectin genes were retrieved from the MSU database  (Kawahara et al. 2013 ) and used for the construction of the map. Gene expansion through segmental duplication or tandem duplication was analyzed for the japonica lectin sequences. Tandem duplications were assigned if: 1. Both genes belong to the same lectin family, 2. There are no more than ten intervening genes and 3. They reside on the same chromosome. Segmental duplications were identified using the Plant Genome Duplication Database (PGDD)  (Lee et al. 2013). Collinear blocks within the O. sativa subsp.
Genomic analysis of the O. sativa EUL genes The genomic structure of the OsEUL genes was analyzed in GenomeView  (Abeel et al. 2011 ) using the japonica RGAP release seven sequence. Visual representation of the genomic structure of the EUL genes was made in IBS 1. 0. 1 (http://ibs. biocuckoo. org/). Sequences of the intron boundaries were analyzed by Weblogo (http://weblogo. berkeley. edu/logo. cgi). Single nucleotide polymorphisms (SNP) in the EUL genes between different rice subspecies were identified using the SNP-Seek database containing SNP genotyping data (called against Nipponbare reference Os-Nipponbare-Reference-IRGSP-1. 0) from the 3000 Rice Genomes Project  (Alexandrov et al. 2015). The use of the Nipponbare reference allele in the different rice groups (in %) is represented in a heat map using the BAR HeatMapper Plus Tool (http://bar.
Molecular modeling of OsEULs Homology modeling of the β-trefoil domains of EULs was performed with the YASARA Structure program  (Krieger et al. 2002) , running on a 2. 53 GHz Intel core duo Macintosh computer. Up to eighteen different models were built up for each of the EUL domains corresponding to LOC_Os07g48500. 1 (OsEULS2), LOC_Os01g01450. 1 (OsEULS3), LOC_Os07g48490. 2 (OsEULD1A), LOC_Os03g21040. 2 (OsEULD1B), and LOC_Os07g48460. 1 (OsEULD2) sequences, using the Xray coordinates of different bacterial hydrolases and lectins as templates: a β-l-arabinopyranosidase from Streptomyces avermitilis (PDB code 3A21)  (Ichinose et al. 2009) , the HA-33/HA-17 hemagglutinin complex of Clostridium botulinum (PDB code 5B2H)  (Sagane et al. 2016) , the mosquitocidal holotoxin from Bacillus sphaericus (PDB code 2VSE)  (Treiber et al.
2008 ), a computationally reconstructed β-trefoil subdomain module (PDB code 3PG0)  (Broom et al. 2012) , the HA33 neutotoxin from Clostridium botulinum (PDB code 4OUJ)  (Lee et al. 2014) , the 1,3Gal43A exo-β-1,3-galactanase from Clostridium thermocellum (PDB code 3VSF)  (Jiang et al. 2012a) , the β-trefoil lectin HA33/C from Clostridium botulinum type C neurotoxin (PDB code 3AJ5, 3AJ6)  (Nakamura et al. 2011) , the retaining xylanase from Streptomyces olivaceoviris E-86 (PDB code 2D24)  (Suzuki et al. 2009 ), a family ten xylanase from Streptomyces olivaceoviridis E-86 (PDB code 1V6W)  (Fujimoto et al. 2004) , and the hemagglutinin of Clostridium botulinum (PDB code 3WIN)  (Amatsu et al. 2013 ). These templates were selected based on the best BLAST E-values found for the amino acid sequences matching with the query sequence, using a cutoff value of 0.
Expression analysis of EUL genes based on publicly available resources The accession numbers of the representative transcripts of OsEULs were downloaded from Rice Annotation Project Data Base (RAP DB) (http://rapdb. dna. affrc. go. jp/). A search for changes in the expression of all rice EULs transcripts was performed in TENOR database (http:// tenor. dna. affrc. go. jp/). Data for the fold change after drought, osmotic stress, abscisic acid (ABA) and jasmonic acid (JA) were downloaded and represented graphically. During our search in TENOR we found three representative transcripts for OsEULS3 and two for OsEULD1A. Since all transcripts for each of these OsEULs showed very similar results, their fold change was represented as mean values.
(http://bioinformatics. intec. ugent. be/blsspeller/) (De  Witte et al. 2015)  and DNaseI-hypersensitive sites downloaded from  Zhang et al. (2012). Only the motif matches that overlap by 50% with the above functional regions (open chromatin or conserved non-coding sequences) were retained (using intersectBed -f 0. 5 from BEDTools;  Quinlan and Hall 2010). For the second approach motif enrichment analysis was used. Motif mapping information was combined with a set of up to 200 co-expressed genes (according their Pearson correlation coefficient)from the TENOR database  (Kawahara et al. 2016). With the help of the RAP-DB ID convertor tool (http://rapdb. dna. affrc. go. jp/ tools/converter) the RAP DB identifiers were converted to MSU locus numbers and used for the subsequent motif enrichment analysis.
During the process of conversion some of the annotated RAP DB genes were not identified in the MSU database which reduced the number of co-expressed genes to approximately 150 genes for each OsEUL gene. Motif enrichment on these coexpressed gene was determined using the hypergeometric test with false discovery rate correction. Only significantly (q-value < 0. 05) enriched motifs also present in the OsEUL promoters were retained as enriched motifs. The GO enrichment analysis on the different OsEUL regulons was performed using the GO enrichment tool in the PLAZA workbench of PLAZA 3. 0 Monocots  (Proost et al. 2015).
Phylogenetic analysis Sequences corresponding to the different EUL domains of the OsEULs were extracted and aligned with MUSCLE using the default settings  (Edgar 2004 ). Based on this alignment a maximum likelihood phylogenetic tree was build using RAxML v8. 2. 4. RAxML used the GTRGAMMA model with automated determination of the best amino acid substitution model (i. e. the model with the highest likelihood score on the starting tree), random number seed and distinct starting trees. Bootstrap iterations to assess the robustness of the generated trees were decided automatically by RAxML. The phylogenetic tree was displayed with FigTree v1. 4. 2 (http:// tree. bio. ed. ac. uk/software/figtree). To extend the phylogenetic analysis to other species, putative EUL sequences were identified by BLAST searches with the lectin domains of the japonica hits against the genomes of 8 monocot and 7 dicot species available from EnsemblPlants (http:// plants.
Proteins with an annotated EUL domain were retained for further analysis. Sequences with a truncated EUL domain were omitted from the analysis. The EUL domain of all putative EUL sequences from the monocot and dicot species under study was extracted and aligned with MUSCLE using the default settings  (Edgar 2004). A logo of the EUL domain was generated with WebLogo 3 (http://weblogo. threeplusone. com/ create. cgi). From the aligned sequences a maximum likelihood phylogenetic tree was built with RAxML v8. 2. 4 as described before. The FigTree v1. 4. 2 software was used to visualize and edit the phylogenetic tree. Reconciliation of the phylogenetic tree with the species tree was performed in Notung 2. 9  (Stolzer et al. 2012). The species tree, containing all species from which EUL domains were sampled, was constructed in NCBI taxonomy (https://www.
Dataset The images and manual landmarking data belong to the CANDELA initiative (Consortium for the Analysis of the Diversity and Evolution of Latin Americans), an international multidisciplinary project including geneticists, anthropologists, statisticians, bioinformatics, and social-anthropologists interested on Latin American populations biodiversity and socio-cultural environment  [43]  (https://www. ucl. ac. uk/candela. The dataset is property of CANDELA and for privacy reasons cannot be made openly available. ). CANDELA has a database containing 7500 individuals that were photographed following a protocol for taking standardised photographic data. The images consist of a lateral view of the head, with a 2136 × 3216 pixel resolution, taken with no specific illumination conditions, and without background removal.
The provided dataset contains 2735 images, one image per individual, each with 45 landmarks and semi-landmarks provided by human operators. It was split into a training set with 2051 images (75%) and a validation set of 684 images (25% of the full dataset), both sets selected with a random permutation crossvalidation iterator. The dataset selection was restricted to the availability of pairs (image, landmarks) from the CANDELA database, images without landmarks associated were excluded for the training and validation steps. The landmarks and semilandmarks used for training and testing were digitised and processed manually using TPSDig and TPSUtil (http:// life. bio. sunysb. edu/morph/). In some of the images, ears were partially occluded by hear, earrings were present, or the illumination and background were not uniform.
Preprocessing To reduce unnecessary processing burden in the ConvNet, an initial rough ROI is priorly located around the candidate areas in the images. During training, the ROI was located using the landmark positions themselves. During automated landmarking, the ROI is found using the Viola and Jones  [44]  general object detection framework. The Haar cascade filter was trained with 133 positive and 667 negative regions (The Haarcascade file trained can be downloaded from https://github. com/celiacintas/tests_landmarks/ blob/master/files/cascade_lateral_ears. xml). As a validation set for this filter, 185 images from the CANDELA dataset were randomly selected. In 92. 43% of the cases, the ROI was correctly found, in 1. 62% of the cases the ROI was not found, and in 5. 95% of the cases the ROI was wrongly placed.
ConvNets architecture and training Three different ConvNet architectures were designed and trained for performing an automatic landmarking task, namely to detect and identify anatomic parts of the ear in image datasets. These architectures are different to each other in the number of convolutional layers, the filter sizes, and the learning rates. A single-channel profile ear image of size 96 × 96 pixels, with brightness scaled to [0, 1], is taken as input. In Fig. 3 , the best performing architecture (Arch0) is shown. The underlying architecture consists of two convolution layers with square filters, followed with a max pooling and dropout layer. This structure is repeated three times to obtain features at different levels of abstraction, with different filter size, number of feature maps, and probability values.
The convolutional layers C1, C2, C5, and C6 have 32 filters of size 4 × 4 and 3 × 3. Layers C9 and C10 have 64 filters of size 3 × 3. All max pooling layers are of size 2 × 2, and the probability values used for D4, D8, D12, and D14 are (resp. ) 0. 1, 0. 2, 0. 3, and 0. 5. After the feature extraction layers, the architecture contains two fully connected linear layers with 1500 units each (F13 and F15 in the diagram), and a dropout layer in between (D14). The output layer contains 90 output units (45 [x, y] pairs) for the predicted position of the landmarks and semi-landmarks. The implementation used Python and the Lasagne library  [45]  (The code is available at https://github. com/celiacintas/tests_landmarks/ blob/master/testing_output_ears. ipynb. ). This allows the use of GPU acceleration without considerable programming effort.
Overfitting reduction ConvNets usually have a huge number of learnable parameters, 8. 622. 970 in the case of our model. Due to the limited size of the training set, overfitting is almost certain to occur. The network will tend to memorise the training examples instead of finding abstractions therein, because it has enough memory to do so. This obviously will not generalise well to new data. Two primary ways to deal with overfitting were applied in our training: Data augmentation: We artificially enlarged the dataset using label-preserving transformations. At random, some of the pictures and the associated landmarks were mirrored about the x-axis and added to the training set (see for instance Fig. 4 ). Regularisation: The model complexity was penalised through the use of dropouts  [46] , which consists of setting to zero the output of each hidden neuron with a certain probability.
5. Fig. 9  shows the predicted landmarking over previously unseen images in the validation dataset. Note that even though some of these images are partially occluded by hair, the landmarking is still sound. The full test set landmarked by the ConvNet, the full structure, and an analysis of the net's behaviour can be seen in https://github. com/celiacintas/tests_landmarks/blob/ master/testing_output_ears. ipynb. Also a subset of landmarked ears over public datasets can be seen in Figs. 6 7 8 9. Despite that our landmarking system was not trained with examples of rotated ears, it still works well with the AMI ear database that includes such cases. To assess the performance of the trained network, a complete landmarking workflow was deployed in a conventional PC hardware (single core Intel i7-5500 2.
40 GHz). In this conventional hardware, a typical automated landmarking requires 4. 68 ms in average. Landmarking a batch of 684 images required 1. 04 s. The landmarking performance is comparable or above the quality of an assisted procedure. Also a similar network was trained for full profile faces, without the ROI extraction preprocessing step. In average, the results were r 2 = 0. 884, RMSE = 1. 365, EV = 0. 951, and Pearson correlation = 0. 976. The automated landmarking results on whole head images were tested on a randomly chosen subset of the CVL public dataset can be seen in Fig. 10. The landmarking behaviour was also tested on less controlled images. A randomly chosen subset the original CANDELA 2136 × 3216 whole head images dataset, without controlled illumination or background removal, was automatically landmarked, and the results can be seen in Fig.
A total set of 8354 images where automatically landmarked with our previous ConvNet detailed in the Section 3. 5, after which a generalised Procrustes fit was applied (see Section 3. 1) to remove translation, scale and rotation effects in the landmarks. In Fig. 12 , a subset of the training dataset can be seen. The training set of the ERT included 6683 feature vectors v i , each consisting of the 45 automatically generated landmarks (v i = [x 0 , y 0 , , x 44 , y 44 ]) and the target values t with a label associated to the person. The remaining 1671 samples were saved for testing. The recognition scores were remarkably high (in average, precision 0. 95, recall 0. 90, f1-score 0. 91, and adjusted rand score 0. 93). The confusion matrix over a subset of test data can be seen in Fig. 13. We performed stratified K-fold with 10 iterations and a test size of 20% of the sample, yielding an accuracy mean score of 0.
We demonstrate the advantages of our method over other split-aligners, by applying it to the problem of identifying medium to large-sized deletions (≥20 bp) from typical human genome resequencing datasets, both simulated and real. We also contrast our technique of bolstering otherwise ambiguous split alignments by combining read group and paired-end information to the conventional method of detecting deletions through discordant alignments of pairedend reads. Quite surprisingly, we find that these conventional methods have lower sensitivity in practice, as they miss a majority of deletions in their attempt to account for variability in fragment size. We show that our method incurs only a small computational overhead and maintains an overall running time that is well within the range of contemporary methods.
Instead, we utilize information from local pairwise alignments of the reads to construct C, using the following principle: all positions aligning to the same position in G contribute to the same column of C (see SI-S5 for more details). Step 4: Joint alignment. Next we align C to G using the scoring scheme described in Equation (  1 ). We devise a dynamic programming algorithm to find a maximum-scoring pair A, H, as a simple extension to the traditional pairwise semi-global alignment. Recurrence formulas are given in SI-S3. The time complexity of our exact dynamic programming algorithm is O(|G| 2 • •|D|), which is not practical for large datasets. To reduce time consumption, we use a speed-up heuristic (see SI-S5) that restricts the dynamic programming only to regions surrounding the candidate SV sites enumerated in Step 2.
Datasets We used both simulated and real datasets for evaluation. Our simulated dataset is based on the set of highconfidence variants in the diploid genome of a single individual (J. Craig Venter)  (8). Since this dataset is derived from Sanger sequencing, it avoids the ambiguity issues faced by short erroneous reads, making it ideal for benchmarking of split alignments. We reconstructed Chromosomes 1 and 2 of the diploid Craig Venter genome from hg19 using a workflow similar to  (22). Single-end and paired-end error-free sequencer reads of 100bp were simulated by random draws from the reconstruction. We chose the read length to be 100 bp as it is still the most commonly used setup for genome resequencing -take for instance, the last published SV annotations within the 1k genome project  (23).
To reproduce the proportion of low quality reads of real sequencing experiments, errors were then introduced according to the perbase phred quality values observed in a real dataset (dataset SRR067577, see SI-S7). We emphasize that this benchmarking scheme is much more fitted to the challenges of SV identification than simply placing variants randomly, accounting only for their length distribution, as the latter would grossly underestimate the proportion of ambiguous regions (Supplementary Figures  S1  and S6 ). In addition, this setup allows us to sequentially assess the effect of two major problems arising in split alignment: ambiguities due to genomic repetitions and artifacts from sequencer errors. The datasets are available for download at the JRA project homepage. Additionally we used the short read dataset SRX652547 generated from the CHM1 cell line, which is derived from a haploid genome.
Evaluation of accuracy For the simulated dataset, the set of positive examples consists of all deletions from the C. Venter genome located in Chromosomes 1 and 2 and spanning at least 20bp (2130 deletions). For the real dataset of CHM1, we compare the predicted deletions to the list of deletions derived from PacBio sequencing (11273 deletions). The length distribution of the deletions for the two genomes are compared in Figure  4A  and B. For most deletions, it is not possible to unambiguously pinpoint its start and end coordinates. We describe this issue in SI-S1. 2, where we associate each deletion with breakpoint ambiguity intervals. We count a predicted deletion as true positive if its start and end coordinates are within the breakpoint ambiguity intervals of a true deletion. Breakpoint ambiguity also means that multiple predictions may in fact be pointing to the same deletion event, and we make sure to avoid such multiple-counting issues when reporting the number of true positives.
Performance of deletion calling We first assessed the performance of the four splitalignment tools at various coverage values for 100bp reads under two simulation scenarios: no sequencing errors and with an error profile derived from the real dataset SRR067577. For Splazers and Segemehl, we used the scripts provided by the authors to make deletion calls (SI-S8). For LAST, we use as threshold the maximum error probability over the local alignments constituting the split-alignment, and identify as redundant, deletion calls with matching start and end coordinates. Overall, JRA shows the best performance over the whole range of simulations and parameter values (Figure  3 ). Splazers, due to its exhaustive search strategy, manages to detect, in some cases, slightly more deletions but at the cost of many more false calls.
At the same level of false positive (FP) predictions (coverage 10, 250 FP, JRA error probability of 0. 08), we observed that more than a third of the true deletions called by JRA are only supported by one read (60% by less than two reads) in the Splazers predictions. Additionally, the error probability reported by JRA integrates the uncertainties of alignments of single reads at the SV site, enabling the user with a more accurate choice when deciding a threshold. Of the tools we compared, only LAST reports a similar probability, but it does not incorporate group information. We compared the sensitivity and the PPV between JRA and LAST for varying probabilities, and JRA had consistently better sensitivity and slightly lower PPV at the same probability threshold (Supplementary Figure  S12 ) We also evaluated the accuracy of the sequence of the deletion locus reconstructed by JRA from the dataset with sequencing errors.