dataesr/scientific-paragraphs-categorization huggingface dataset:
Instrumentation and data collection Only the right (dominant) side of each participant was evaluated during the box lifting task, assuming that the right and left sides of the upper body moved symmetrically  [24]. Based on the upper-limb kinematic model developed by Jackson et al. [25] , 25 reflective skin markers were placed on the thorax, right clavicle, scapula, humerus, forearm, hand, and on the box used for the lifting task (Fig  1 ). Trials were recorded using an 18-camera Vicon TM motion analysis system at 200 Hz (Oxford Metrics Ltd, Oxford, UK). Surface EMG electrodes (Trigno TM EMG Wireless System, Delsys, USA; 1000 Hz) were positioned on nine muscles: biceps and triceps brachialis; anterior; middle and posterior deltoid; latissimus dorsi superior head; upper, middle and lower trapezius, according to SENIAM recommendations  [26].
With the inclusion of new members, understanding about evolutionary mechanisms and processes by which members of the proposed order, Megavirales, have evolved has become a key area of interest. The central role of gene acquisition has been shown in previous studies. However, the major drawback in gene acquisition studies is the focus on few MV families or putative families with large variation in their genetic structure. Thus, here we have tried to develop a methodology by which we can detect horizontal gene transfers (HGTs), taking into consideration orthologous groups of distantly related Megavirale families. Here, we report an automated workflow MimiLook, prepared as a Perl command line program, that deduces orthologous groups (OGs) from ORFomes of Megavirales and constructs phylogenetic trees by performing alignment generation, alignment editing and protein-protein BLAST (BLASTP) searching across the National Center for Biotechnology Information (NCBI) non-redundant (nr) protein sequence database.
Phylogenetic methods enable detection of gene transfers either (i) by reconstructing the gene tree and comparing it with the reference species tree, often via statistical analysis like bipartitions, quartet bipartitions, etc. [27, 28] ; or (ii) by examining gene history, through phyletic profiles  [29]. Several algorithms like LatTrans  [30] , HorizStory  [31] , Efficient Evaluation of Edit Paths  [32]  and PhyloNet package including RIATA-HGT  [33]  and T-REX  [34]  are available to detect HGT events. Members of the proposed order Megavirales have been shown over the past decade to be very common in our environment  [35, 36]  and giant viruses were also detected in humans and suspected to be potential human pathogens  [37] [38] [39]. Therefore, the need for understanding the major evolutionary forces guiding the evolution of Megavirale genomes and their gene history is growing, which makes it necessary to adopt some systematic searching for identifying the role of reticulate evolutionary events like HGT in evolution of distantly related Megavirale families.
A General Overview of Workflow Our workflow inputs whole ORFomes in FASTA format (user-defined) and executes the following basic steps in sequential order: orthologous cluster identification, reference species tree construction, selection of representative sequence from cluster, searching for homologs of the representative sequence using protein-protein BLAST (BLASTP) in local database, filtering of the blast hits, retrieval of taxonomic lineage information, coding BLAST hits with taxonomy identifier (TaxID), alignment generation from filtered BLAST output, maximum likelihood (ML) tree generation from alignment, preparation of species tree for each gene tree, and finally, detection of HGT event (Figure  1 ). Some steps in the workflow involve automated curation and filtering of dataset for ease of use and understanding, which will be discussed in following paragraphs.
Complete ORFomes The ORFomes for 86 completely sequenced members of 10 families (3 ascoviruses, 1 asfarvirus, 1 faustovirus, 16 iridoviruses, 3 marseilleviruses, 5 mimiviruses, 2 pandoraviruses, 18 phycodnaviruses, 1 pithovirus and 36 poxviruses) within the order Megavirales were downloaded from the National Center for Biotechnology Information (NCBI). The details regarding NCBI accession numbers and download link for these genomes are provided in Table  S1.
The inflation index of 1. 5 was used to regulate cluster tightness (granularity), and the resulting clusters of orthologous groups were analyzed further (output 1). Orthologous group information (output 1, Tables  S1  and S2 ) was used to construct the reference Megavirale family tree using distance super-matrix approach. Amino acid sequences of each OG were retrieved from ORFome sets and they were further subjected to alignment generation using MUSCLE  [44]  followed by distance matrices generation by deploying 'protdist' program from Phylip package  [45]. The computational efficiency and ease of use of distance-based tree reconstruction methods make them an attractive option for large data sets, where they can provide a snapshot of the underlying evolutionary relationships quickly and easily.
In these cases of two copies of orthologous genes in the same genome, we included the paralog with the best bit score in the corresponding cluster. After removal of paralogs from the OGs, we selected the representative sequence from each cluster-longest sequence in cluster was selected to increase the phylogenetic spread of homologs in non-redundant (nr) database-and carried out BLASTP search to detect homologs. Viruses 2017, 9, 72 4 of 15 representative sequence from each cluster-longest sequence in cluster was selected to increase the phylogenetic spread of homologs in non-redundant (nr) database-and carried out BLASTP search to detect homologs.
Search for Homologs and Curation of Blast Output BLASTP  [42]  was used against local nr database (last accessed on 14 January 2016) to find potential homologs of each representative sequence (scoring matrix BLOSUM62, word size 3, low complexity region filtered out). An E-value cutoff equal to or less than 10 -5 was set to filter out possible contaminants from blast output and hits below the threshold of 50% query size (coverage) plus 30% identity were further discarded. Since, we took one representative from each cluster, other sequences present in OG were automatically re-picked from nr database. A tabular output is generated with taxonomic lineage information of each significant hit (information such as TaxID, species name, etc. , were retrieved). BLAST is currently the most common way of assessing homologous sequences, however, it can return identical hits (such as highly similar sequences from different strains of same species, allelic variations, paralogs, etc.
) that do not add information to the analysis but instead make the final analysis more time-consuming. Thus, to make BLAST tabular output more feasible and easy to use, we removed such sequences from the Blast output file. From hits of each queried sequence, paralogs from same species were removed by selecting the hit with the best bit score. Also, hits from different strains of same species were removed and one with the best bit score was selected. Amino acid sequences of significant hits were retrieved in FASTA format using blastdbcmd utility from nr database for each OG and taxonomic lineage information was extracted using corresponding TaxID. For keeping track of the names in this large dataset, we complimented the FASTA headers with their TaxIDs; thus, each OG had their unique homologous protein set in FASTA format coded with their TaxIDs.
Alignment Generation, Alignment Editing and Tree Preparation Both protein sets (containing amino acid sequences of optimal BLAST hits) were then queried in alignment program MUSCLE  [44]  to generate the alignment for each orthologous group, which is executed with the program's default settings. After generation of alignment, Gblocks  [48]  was used for the removal of poorly aligned positions and highly divergent regions. Maximum likelihood (ML) trees from each such alignment were constructed using FastTree  [49]  using WAG  [50]  model of amino acid evolution. FastTree is optimized to work with large dataset and considered to be more accurate than many of other standard ML-based phylogenetic tree construction algorithms such as PhyML 3. 0 or RAxML 7  [49, 51]. At this stage of workflow, we were able to generate gene trees in Newick format of those OG which are specific (output 3) and non-specific to Megavirales.
After curation, output 4 was generated containing the information of horizontally transferred gene families (OGs) with information of putative donor and receptor. Gene trees in which a HGT event was not detected were then further mapped with BLAST tabular output which contains taxonomic lineage information of each homologous hit, by which we are able to extract information about other cellular domains present in tree, thus generating output 5, which include gene trees of those OGs which have common origin with other cellular domains of life. Gene trees which are generated at the end of this workflow (output 3, output 4, output 5) were in Newick format. So, in order to visualize the proper midpoint rooted and color coded phylogenetic trees, a python script was implemented in ETE toolkit  [52].
For this purpose, another annotation file was prepared manually from taxonomic lineage information of Blast output. Here, for each taxon present in BLAST output, we prepared a tabular file containing columns with species name; cellular domain-Eukaryote, Bacteria, Archaea, Virus, Phages, Megavirales included in dataset, double-stranded DNA (dsDNA) (other Megavirales not included in dataset); group and lineage (highest possible taxonomic level).
Implementation MimiLook is a Perl (version 5. 18. 2) command line program developed on a 64-bit Linux architecture, currently running on Ubuntu 14. 04. 1 LTS. Before its commencement, programs like OrthoMCL  [41] , Muscle  [44] , protdist  [45] , SDM  [46] , fastME  [47] , Gblocks  [48] , FastTree  [49] , T-REX  [34]  were installed since the workflow requires calling these algorithms. More details of these programs are available in their respective installation documents. Local blast nr database was downloaded and installed. Also, the workflow requires ETE toolkit  [52]. The input for MimiLook is a directory containing ORFomes to be analyzed in FASTA format and the final output is a directory containing gene trees (Newick format), blast tabular output, reference species tree (Newick format) and text file with information of HGT events.
Results of MimiLook Analysis in the Proposed Order Megavirales By using 86 ORFomes of Megavirales members classified in 10 families as input, this automated Perl command line program identified several gene families (i. e. , OGs) which are specific to Megavirales and several other gene families sharing origin with cellular domains of life. The present dataset (containing nearly 29,000 proteins from all the ORFomes) took approximately 48 h on a 64-bit Linux machine to yield the output with minimal human intervention. The final results are stored in a directory containing various outputs enclosing information about the evolutionary scenarios of the OGs. We further evaluated the results according to the outputs generated in the various steps of this workflow. S2 ). A total of 7898 proteins remained unclustered and not included in further analysis.
The majority of OGs (4168) were found to be family specific (i. e. , represented by species classified in one Megavirale family only), whereas, 409 OGs (approximately nine percent) were found to contain proteins from two or more Megavirale families (Figure  2A ). Out of 4168 family specific OGs, 396 OGs were found to be represented by paralogs from same species i. e. , they are present in only one species of one family (Table  S3 ). An earlier study  [9]  was done with 45 NCLDV genomes from 7 families, whereas in this study 86 MV ORFomes from 10 families (three new putative families: faustoviruses, pandoraviruses and pithovirus) were included. Disparity is seen with the previously published NCVOG study mainly due to a larger dataset used in our analysis, but the pattern of results, such as large percentage of family specific OGs, large percentage of OGs shared by two species, more family specific OGs present in Mimiviridae and Phycodnaviridae, etc.
, are in accordance (Figure  2B ). S2 ). A total of 7898 proteins remained unclustered and not included in further analysis. The majority of OGs (4168) were found to be family specific (i. e. , represented by species classified in one Megavirale family only), whereas, 409 OGs (approximately nine percent) were found to contain proteins from two or more Megavirale families (Figure  2A ). Out of 4168 family specific OGs, 396 OGs were found to be represented by paralogs from same species i. e. , they are present in only one species of one family (Table  S3 ). An earlier study  [9]  was done with 45 NCLDV genomes from 7 families, whereas in this study 86 MV ORFomes from 10 families (three new putative families: faustoviruses, pandoraviruses and pithovirus) were included. Disparity is seen with the previously published NCVOG study mainly due to a larger dataset used in our analysis, but the pattern of results, such as large percentage of family specific OGs, large percentage of OGs shared by two species, more family specific OGs present in Mimiviridae and Phycodnaviridae, etc.
e. , no homologs from organisms other than Megavirales are available in nr database by using our cut-off and stringent filters (Figure  4 , Table  S4 ). Output 3: This output includes those query sequences which have homologs present only in Megavirales. These hits were aligned and gene trees were prepared for each individual OG. We identified 3639 (approximately 80%) OGs (gene families) specific only to Megavirales, i. e. , no homologs from organisms other than Megavirales are available in nr database by using our cut-off and stringent filters (Figure  4 , Table  S4 ). From total of 4577 OGs clustered, 3639 (80%) were found to be MV specific; 418 were involved in horizontal transfer (9%); 368 were sharing clades with homologs of other domains of life (8%), 100 (3%) were considered ambivalent and 52 had sympatric associations.
Discussion and Future Perspective Several approaches can be considered to detect incidence of HGT  [26] [27] [28] , however, the most convincing method to detect HGT uses phylogenetic inference  [53]. Highly supported topological incongruence between a strongly supported gene tree and the known species tree can be penuriously explained by a reticulate evolutionary event like HGT  [54]. Currently, few algorithms are available to detect HGT  [29] [30] [31] [32] [33] , but no workflow has been devised yet for searching systematically gene acquisition in large dataset of clustered orthologs. MimiLook described here is such an attempt to elucidate the evolutionary scenarios in user specific ORFomes, especially among MVs. It not only detects the instances of gene acquisition, but also allowed us to glance into other evolutionary patterns like phyla specificity and provided information about shared ancestry.
Experimental evidence indicates that about 60% of miRNA-binding activity does not follow the canonical rule about the seed matching between miRNA and target mRNAs, but rather a non-canonical miRNA targeting activity outside the seed or with a seed-like motifs. Here, we propose a new unbiased method to identify canonical and non-canonical miRNA-binding sites from peaks identified by Ago2 Cross-Linked Im-munoPrecipitation associated to high-throughput sequencing (CLIP-seq). Since the quality of peaks is of pivotal importance for the final output of the proposed method, we provide a comprehensive benchmarking of four peak detection programs, namely CIMS, PIPE-CLIP, Piranha and Pyicoclip, on four publicly available Ago2-HITS-CLIP datasets and one unpublished in-house Ago2-dataset in stem cells.
We measured the sensitivity, the specificity and the position accuracy toward miRNA binding sites identification, and the agreement with TargetScan. Secondly, we developed a new pipeline, called miRBShunter, to identify canonical and non-canonical miRNA-binding sites based on de novo motif identification from Ago2 peaks and prediction of miRNA::RNA heteroduplexes. miRBShunter was tested and experimentally validated on the in-house Ago2-dataset and on an Ago2-PAR-CLIP dataset in human stem cells. Overall, we provide guidelines to choose a suitable peak detection program and a new method for miRNA-target identification.
Despite the biological importance of the miRNAs, little is known about the specificity of miRNA binding sites on mRNAs  (5). Different bioinformatics programs, such as miRANDA, TargetScan and PITA, use algorithms to predict canonical miRNA-binding sites based on different features, which include sequence complementary matches between miRNA seed and 3'UTR, conservation of the target sequence across species, low free-energy of the predicted duplex and degrees of complementary matches beyond the seed sequence. Another method, called MIRZA, calculate the strength of the miRNA-mRNA heteroduplexes found in Ago2 CLIP-seq datasets by using 27 different energy parameters  (6). MIRZA uses these parameters to predict the frequencies with which miRISCs bind to mRNA fragments in the mRNA pool  (6). Although some experimentally validated miRNA-target mRNAs were successfully identified by using the support of these programs in combination with mRNA profile analysis, this approach can mislead to high rates of false positive and false negative targets  (7, 8).
Although the PAR-CLIP and iCLIP provide higher resolution, the HITS-CLIP method is more largely used because of its wide adaptability, relatively easy sample preparation and the straightforward bioinformatics analysis  (14). For this reason, here we focused on HITS-CLIP data analysis. In this study we have developed a new method to identify miRNA-binding sites from CLIP-seq datasets, called miRBShunter. This method allows a comprehensive unbiased identification of both canonical and non-canonical miRNA-binding sites defined by the de novo identification of enriched motifs from Ago2 CLIP-seq peaks and the calculation of a miRNA::RNA heteroduplex score. The search for enriched de novo motifs permits the identification of miRNA binding sites, without restricting the analysis on the seed region, as other methods propose  (15) (16) (17) (18).
Since the quality of the detected peaks is very important, to ensure a reliable output of the pipeline, first we have performed a comprehensive, quantitative and qualitative comparative evaluation of four different publicly available programs for HITS-CLIP peak detection, including CIMS, PIPE-CLIP, Piranha and Pyicoclip on four published Ago2 HITS-CLIP datasets  (10, (19) (20)  and on an unpublished in-house Ago2 HITS-CLIP dataset generated from P19 stem cells. By tuning the programs in default parameters, we found that Pyicoclip outperformed the other programs in terms of sensitivity, positional accuracy, agreement with TargetScan, specificity, as well as for consistency in finding the same results on different databases from the same tissue. Based on the benchmarking, we secondly validated the proposed method by running Pyicoclip-detected peaks of the in-house Ago2 HITS-CLIP dataset on a new developed pipeline, called miRBShunter.
We found 40 potential canonical and 75 noncanonical miRNA binding sites with either a miRNA seedlike motif or outside the seed. Among them, we experimen-tally validated six new miRNA-binding sites (three canonicals and three non-canonicals) out of nine by gene reporter assay. To demonstrate that the method works independently on the CLIP-seq technique used, we tested it on an Ago2 PAR-CLIP dataset from human stem cells  (21). miRBShunter pipeline found 215 canonical and 611 noncanonical miRNA binding sites and we have experimentally validated 5 out of 7 (3 canonicals and 2 non-canonicals), confirming the validity of our method. Therefore, the proposed method can be usefully used for detection of both canonical and non-canonical miRNA binding sites. Finally, we have developed a user-friendly tool with a graphic interface to use miRBShunter available at https://github.
Publicly HITS-CLIP-seq datasets Raw sequencing reads for all datasets except for the inhouse one were downloaded by GEO  (Leung et al. (20) : GSE25310, Karginov et al. (19) : GSE4404) and by the website: http://ago. rockefeller. edu/rawdata. php for Chi et al. (10)  datasets. Pre-processing and alignment for all datasets were done following the protocol in Moore et al. (14). Briefly, reads were mapped on either the mouse (mm10) or the human (hg19) genome using the program Novoalign (http://www. novocraft. com/products/novoalign/). In Supplementary Table  S1  is reported the number of mapped reads for each replicate of each dataset. The reproducibility of the replicates was assessed by performing principal component analysis using the R package 'htSeqTools' (R package version 1. 14. 0) (Supplementary Figure  S1 ).
Enriched peaks for the Ago2 PAR-CLIP dataset of Lipchina et al. (21)  were downloaded by StarBase web server  (22). A list of most expressed Ago2-associated miRNAs in each dataset is shown in Supplementary Table  S2. For the in-house dataset, we used the web-tool omiRas  (23)  to map the reads against mouse miRNA database and to normalize toward the reads count.
Main characteristics of publicly peak detection programs for HITS-CLIP analysis Briefly, diverse strategies have been employed by different programs to identify peaks. In particular, 'PIPE-CLIP'  (24)  and 'Pyicoclip'  (25)  cluster together reads based on positional overlap, while 'Piranha'  (26) , 'MiCLIP'  (27)  and 'HITS-CLIP data analysis' (http://qbrc. swmed. edu/ softwares. html) bin on genomic portions by fixed size. On the contrary, CIMS focuses on the mere identification of cross-linked sites by looking for clusters containing mutated reads  (28). All these programs follow a statistical procedure to discriminate enriched clusters over the background  (29). Briefly, PIPE-CLIP and Piranha employ the zero-truncated negative binomial likelihoods to model the distribution of the reads in peaks.
These programs also use additional information to refine the peak detection, such as identification of cross-linked-dependent mutations for PIPE-CLIP or the use of covariates provided by the user for Piranha, including cross-linked-dependent mutations and transcript abundance. On the other hand, Pyicoclip performs a background PAGE 3 OF 15 Nucleic Acids Research, 2017, Vol. 45, No. 9 e71 estimation implementing the modified false discovery rate (FDR) procedure proposed by Yeo et al. (30). Particularly, it calculates the background frequency after randomly placing the same number of extended reads within the region and iterating the process many times. MiCLIP and HITS-CLIP data analysis employ Hidden Markow Models (HMM) to model the reads distribution in peaks. Finally, CIMS assesses statistical significance employing a permutation based model.
Peak detection parameters and peak list creation All programs were run with default or suggested parameters, as follows: -PIPE-CLIP: -m 1 -c 0 -l 20 -r 1 -M 0. 001 -C 0. 001 -s mm10/hg19 -Piranha: -b 20 -p 0. 001-d zeroTruncatedNegativeBinomial -Pyicoclip: -P-value 0. 001 -region *. bed -CIMS: parameters as in Moore et al. (14)  Because Pyicoclip requires the user to provide a BED file with the genome regions in which to look for peaks, we took from Homer database the genomic coordinates of the following categories: 3 UTR (UnTranslated Region), 5 UTR, CDS (protein CDS), introns, promoters, TTS (Transcription Termination Site), non-coding RNAs and intergenic sequences  (33). Pyicoclip was run with each BED file separately on each dataset. To avoid any possible duplicates, after filtering (FDR < 0.