dataesr/scientific-paragraphs-categorization huggingface dataset:
Cis-QTL identification Association between gene expression and the genotype at each variant position located in the exons of the APPRIS transcripts was tested in the set of 21 unrelated Yoruban individuals using PLINK v1. 07  (Purcell et al. 2007 ). For each transcript, replicate gene expression measurements were averaged for this analysis. The expression values were regressed on variant genotypes assuming an additive genetic model where genotype was coded as 0,1, or 2 copies of the alternate allele and restricting the testing to variants with a minor allele frequency >10% in the 21 unrelated Yoruban individuals.
Acknowledgments C. C. is supported by the  Child Health Research Institute , the  Lucile Packard Foundation for Children's Health , and the  Stanford CTSA  grant number  UL1TR000093. G. W. B. is supported by a  Benchmark Stanford Graduate Fellowship. S. I. C. is supported by a  Stanford Transformative and Innovation  grant. D. S. was supported by  National Institutes of Health, National Human Genome Research Institute grant  NIH/NHGRI T32 HG000044 , and the  Genentech Graduate Fellowship. Hua Tang  is supported by the  NIH  grant  GM07305907. This research was supported by  NIH  grants  1U01HG00761101 ,  HG007611 , and  HG006996. We thank  Basar Cenik ,  Douglas H. Phanstiel , and  Robert J. Nichols  for comments. We thank  Ghia Euskirchen  for sequencing support. Illumina sequencing services were performed by the  Stanford Center for Genomics and Personalized Medicine.
Several methods have been described for TE annotation  (24, 25)  that can be categorized into two types of strategies: library-based or signature-based  (26). e84 Nucleic Acids  Research, 2015, Vol. 43, No. 13  PAGE 2 OF 9 In the case of the library-based approach, a collection of reference TEs is used to screen genomes to identify similar sequences. This is typically done using RepeatMasker  (27)  and a public TE database such as repbase  (28)  or the MIPS repeat database (29) that contains reference TE sequences from a variety of species. The library-based method has several limitations. While genes are highly conserved between species, TEs diverge rapidly due to the absence of selective pressure. This divergence in TE sequences can limit the performance of the library-based approach. This strategy also presupposes the vertical evolution of TE sequences, yet it has been shown that millions of horizontal transfer events have occurred between divergent plant species over the last few millions of years  (30).
nt-siRNA as guide for TE annotation An overview of the TASR approach is as follows (Figure  1 ): 24 nt-siRNAs were mapped to the genome using a shortread alignment program such as Bowtie2  (38). The 24 nt-siRNAs map preferentially to TEs and their diverged copies in a genome, even though the 24 nt-siRNAs 'mapping profiles' may be complex (Figure  1 -I and 1-II-A). For most TEs in a genome, 24 nt-siRNAs covered the full-length element and correspond to the exact TE boundaries (Figure  1-I-A ). However, 24 nt-siRNAs can spread beyond the TE boundaries, or not cover the entire length of an element (Figure  1-I-B  and 1-I-C ). This could be due to missing 24 nt-siRNAs sequences in the library or because the 24 nt-siRNAs that map to a locus are produced by a diverged TE copy. The success of this approach assumes that at least one member of each TE family generates 24 nt-siRNAs.
We classified the TASR-specific TEs using PASTEC (35) (Supplementary Tables  S1, S2  and S3 ). TE candidates that were not classified by PASTEC were first used as Blast queries against the classified elements since some paralogs are difficult to classify as they have lost some structural features due to fragmentation and deletion. Secondly, we performed a similarity search against a collection of TE sequences including reference elements from Repbase  (28)  and MIPS repeat database  (29). We also used those sequences to Blast against Coding DNA Sequence (CDS) corresponding to host genes. The results of this is shown in Figure  2-B. 71%, 58% and 57% of Arabidopsis, rice and soybean TASR-specific TE fractions, respectively, were assigned to different classes and superfamilies. It is important, however, to note that 9%, 8% and 2% (1.
8%, 1. 6% and 0. 27% from the total TASR TEs) of Arabidopsis, rice and soybean TASRspecific TE candidates produce matches against CDS sequences ranging from few base pair to full length CDS. It is known that in plants some genes can be targeted by the RdDM pathway  (51, 52). Therefore, if an RdDM targeted gene is present in at least two copies it could be falsely identified as TE by TASR. It have been also shown that some TE superfamilies such as Helitrons  (53)  or CACTA  (54)  can capture gene fragments resulting in the presence of CDS sequences inside TEs. Finally, approximately half of the 'host genes' are annotated as 'unknown' or 'hypothetical genes' and some encode proteins, such as reverse transcriptase, helicase or transposase (see Supplementary Tables S4, S5 and S6). Furthermore, the number of duplicated genes is much higher than those falsely identified by TASR.
However, TASR may falsely annotate some gene families as TEs if at least two highly similar members are targeted by 24 nt-siRNAs, such as some gene families (Supplementary Tables S4, S5 and S6). A full understanding of how the different TASR steps affect the final annotation will enable a user to have a better interpretation of TASR-annotated TEs. Since the TASR pipeline is based on 24 nt-siRNAs, the quality of the small RNA library is critical. The tissue used to generate the siRNA library could affect the quality of TE annotation. It is known that 24 nt-siRNAs differentially accumulate in various tissues, especially in flowers and young fruit in Arabidopsis  (56, 57) , thus 24 nt-siRNAs libraries obtained from those organs may be more suitable for the TASR pipeline. We tested TASR using a flower-derived 24 nt-siRNAs library in Arabidopsis but did not observe significant differences versus a leaf 24 nt-siRNAs library (data not shown) indicating that the diversity rather than the abundance of 24 nt-siRNAs is most critical.
In Sagitta elegans, we found significant levels of recombination in the nuclear intron dataset, as would be expected for an interbreeding population of individuals (phi-test, p-value=0). Similarly, 18S and 28S loci show very little variation and no phylogenetic pattern (0 variable sites in S. elegans and 2. 6% in S. cephaloptera, Table  S1  and Dataset S1). While these markers can show little variation between species in other groups, they exhibit significant interspecific divergence in chaetognaths and have thus have been extensively used to resolve intraphyletic relationships  [23] [24] [25]. In sum, we considered three nuclear loci with varying evolutionary rates and none showed patterns of divergence congruent with the observed mitochondrial lineages in either of two species. Hence, we conclude that highly divergent mitochondrial lineages are present within interbreeding populations of Spadella cephaloptera and Sagitta elegans.
(c) Class-level divergence between intraspecific lineages Mitochondrial gene trees for each species and their concatenation from 54 individual mitogenomes show congruent topologies as expected for a haploid, non-recombining genome (Dataset S2). We thus examined phylogenetic relationships and divergence using the complete set of mitochondrial protein-coding sequences along with existing mitogenomic data from other chaetognath species (Figure  2a )  [26] [27] [28]. To put mitochondrial divergence levels into perspective, we compared the phylogenetic distance between chaetognath mitochondrial lineages with those inferred between the main vertebrate groups (Figure  2b ). We found that distances between mitochondrial lineages of Spadella cephaloptera and Sagitta elegans compare with those recovered within amniote and mammalian lineages, which diverged 312 and 170 Myr ago, respectively  [29].
(d) Mitochondrial diversity values represent extremes across animals We established that our nucleotide diversity estimates for two chaetognath species represent the highest values reported so far for any metazoan by surveying public databases for all available single-species population Cox1 datasets (Figure  3  and Table  S2 ). Sagitta elegans shows the highest level of synonymous diversity for a single species (π S = 0. 646) followed by Spadella cephaloptera (π S = 0. 476). Conversely, Spadella cephaloptera is the more variable species at the coding level (π N = 0. 023) followed by Sagitta elegans (π N = 0. 018). We find that both chaetognaths harbor more intraspecific variation than several established cases of extreme mitochondrial diversity driven by (micro-) allopatric divergence, such as the crustacean Tigriopus californicus (π S = 0.
[23]. All amplified fragments were sequenced using Sanger technology in both directions, and for the L36a intron of both species, and 18S and mitochondrial genes of Spadella cephaloptera, the fragments were cloned in pGEM-T easy (Promega) prior to sequencing. All primers employed are specified in Table  S1. All sequences were deposited in Genbank (see Table  S1  for accessions).
Multiplexed libraries were then sequenced on a single Illumina MiSeq run. An average of 71,000 paired-end reads (150bp) was obtained for each fragment library allowing for a median 800× coverage per genome. After de-multiplexing, reads from each library were trimmed for low quality stretches using sickle (available at github. com/najoshi/sickle), then error-corrected and assembled with SPAdes (v2. 5. 0) using BayesHammer error correction and multiple k-mers  (21, 33, 55, 77)   [48]. To check for potential mis-assemblies, reads were mapped back to each mitochondrial genome using Bowtie2  [49]  and corresponding alignments were inspected by eye in IGV viewer  [50]  to check even coverage and concordance of pair-end information with the assembled sequence. The scaffold with highest coverage was selected and compared with Cox1 and Cox2 fragments previously obtained.
(d) Molecular evolution and population genetic analysis Standard population genetic parameters were calculated from nucleotide datasets using the Eg-gLib python library  [51]. To show evolutionary relationships of mitochondrial haplotypes, we used Neighbor-Net Networks from Splitstree4 with K2P distances  [52]  (Figure  1  and Figure  S2 ). We also performed Maximum Likelihood (ML) phylogenetic inferences using RAxML assuming the GTR+Γ model for nucleotide datasets and the MtZoa+ Γ model for amino acid datasets  [53, 54]  (Figure  2a ). Saturation levels in each marker were evaluated for both amino acid and nucleotide data by comparison of pairwise distances under a simplistic model of evolution (Kimura protein, K2P nucleotide) and the patristic ML distance (GTR+Γ or MtZOA+Γ). Non-synonymous and synonymous substitution rates were estimated following distinct approaches using the PAML package  [55] : (i) pairwise comparisons were conducted on each gene using the yn00 program  [56] , (ii) to account for potential lineage specific effects, the 'branchmodel' was employed in codeml assuming a different d N /d S ratio in each lineage for S.
(e) Survey of mitochondrial genetic diversity in metazoans To explore the extent of mitochondrial genetic variation in metazoans, we searched the NCBI popset database for datasets corresponding to Cox1 gene fragments (keyword: Cox1, COI, CO1), including a single species and at least 10 sequences. We obtained 1581 records corresponding to 1079 species. We focused on the 437 datasets associated with a referenced publication (pmid) and we excluded datasets corresponding to cryptic species based on keyword search in abstract of papers (keywords: 'cryptic' and 'species complex'), and manual curation, yielding 329 datasets. We retrieved sequences from the corresponding datasets, and built a codon alignment based on the alignment of amino acid translation using Muscle. Distinct fragments of Cox1 were sometimes pooled in the same popset causing misalignment, in which cases we discarded the shorter fragments.
Acknowledgements This research was supported by  NWO-VENI  grant  863. 08. 024  to K. P. and by  ERC  ( FP7/ 2007-2013) / ERC  grant [268513]11 funding F. M. The  Wellcome Trust Centre for Human Genetics (WTCHG)  is funded by  Wellcome Trust  grant  090532/Z/09/Z  and  MRC Hub  grant  G0900747 91070. We thank  L. Friis Møller  and  C. Hörnlein  for help with sampling in Gulmar fjord in Sweden;  E. Goetze ,  M. Taylor ,  L. E. Becking ,  I. Maeso ,  J. Paps ,  W. Renema ,  M. Schilthuizen  and  P. W. H. Holland  for commenting on the manuscript;  E. J. Bosch  for help with illustrations;  P. Kuperus  and  C. Ribout  for technical assistance and the WTCHG for the generation of the sequencing data.
2004 Simon et al. 1994 CO1 CO1univ-F CO1univ-R TRATNGGDGGNTTYGGVAAYTG ACYTCDGGRTGRCCRAARAAYCA This study Spadella 16S 16Sgen-F 16Sgen-R ATTTTCTCTGGTTCAACATCGAGG TTTTTGTATTACGACTAAAGGACCCG This study cephaloptera 18S 18S_F 18S_R AACCTGGTGATCCTGCCA TGCAGGTTCACCTACAGAA Papillon et al. 2006 L36A L36A_F L36A_R TCAGGGATAARGGCTTTCGCAC CGAGGCATCGCTTTTCGTAG Marlétaz et al. 2010 S. elegans & S. setosa 18S 18S-A1F 18S-1800R 18S-KPF 18S-KPR CTGGTTGATCCTGCCAGTCATATGC GATCCTTCCGCAGGTTCACCTACG TGGAGGGCAAGTCTGGTG TTCCCGTGTTGAGTCAAATTAAG Vonneman et al. , 2005 This study 28S 28S-C1F 28S-D3R ACCCGCTGAATTTAAGCAT GACGATCGATTTGCACGTCA Dayrat et al. , 2001 Vonneman et al. , 2005 Dataset S1. Dataset S2.
We present a new, general data structure that reduces the communication cost of recent tree-based ORAMs. Contrary to ORAM trees with constant height and path lengths, our new construction r-ORAM allows for trees with varying shorter path length. Accessing an element in the ORAM tree results in different communication costs depending on the location of the element. The main idea behind r-ORAM is a recursive ORAM tree structure, where nodes in the tree are roots of other trees. While this approach results in a worst-case access cost (tree height) at most as any recent tree-based ORAM, we show that the average cost saving is around 35% for recent binary tree ORAMs. Besides reducing communication cost, r-ORAM also reduces storage overhead on the server by 4% to 20% depending on the ORAM's client memory type.
Unfortunately, closing the gap between current ORAM techniques and the theoretical lowerbound of Ω(log N )  [10]  would require another major breakthrough. Consequently, we focus on practicality of treebased ORAMs. In general, to access an element in a tree-based ORAM, the client has to download the whole path of nodes, from the root of the ORAM tree to a specific leaf. Each node, also called a bucket, contains a certain number of entries (blocks). In case of constant client memory, there are O(log N )  [28]  entries per bucket, otherwise there are a small constant z many entries, e. g. , z = 5  [30]. In any case, downloading the whole path of nodes is a costly operation, involving the download of multiple data entries for each single element to be accessed. Communication cost primarily depends on the height of the tree and, correlated, the number of entries per tree node and the eviction mechanism.
Contrary to recent κ-ary tree ORAMs, in this paper, we propose a new, different data structure called recursive trees that reduces tree height and therewith cost compared to regular trees. In addition to reducing communication overhead, recursive trees also improve storage overhead. Our new data structure r-ORAM offers variable height and therefore variable communication complexity, introducing the notion of worst and best cases for ORAM trees. r-ORAM is general in that it is a flexible mechanism, applicable to all recent tree-based ORAMs and possibly future variations thereof. A second cost factor for a client is the total storage required on the cloud provider to hold the ORAM construction  [21]. For an N element tree-based ORAM with entries of size l bits, the total storage a client has to pay for is at least (2N -1) • log N • l  [28]  or (2N -1) • z • l  [30].
In addition, a "map" translating ORAM addresses to leaves in the tree needs to be stored, too. Technical Highlights: We present a new data structure reducing the average or expected path length, therefore reducing the cost to access blocks. Our goal is to support both constant and polylog client memory ORAMs. Straightforward techniques to reduce the tree height, e. g. , by using κary trees  [8] , require poly-logarithmic client memory due to the more complex eviction mechanism. The idea behind our technique called r-ORAM is to store blocks in a recursive tree structure. The proposed recursive data structure substitutes traditional κ-ary (κ ≥ 2) trees with better communication. Starting from an outer tree, each node in a tree is a root of another tree. After r trees, the recursion stops in a leaf tree.
The worst-case path length of r-ORAM is equal to c • log N , with c = 0. 78, yet this worst-case situation occurs only rarely. Instead in practice, the expected path length for the majority of operations is c • log N , with c = 0. 65 for binary trees. The shortest paths in binary trees have length 0. 4•log N. In addition to saving on communication, the r-ORAM approach also saves up to 0. 8 on storage due to fewer nodes in the recursive trees. To support our theoretical claims, we have also implemented r-ORAM and evaluated its performance. The source code is available for download  [25]. r-ORAM is a general technique that can be used as a building block to improve any recent tree-based ORAM, both with O(1) client memory such as Shi et al. [28] , O(log N ) client memory such as Stefanov et al.
Recursive Binary Trees A Naive Approach: To motivate the rationale behind r-ORAM, we start by describing a straightforward attempt to reduce the path length and therewith communication cost. Currently, data elements added to an ORAM are inserted to a tree's root and then percolate down towards a randomly chosen leaf. As a consequence, whenever a client needs to read an element, the whole path from the tree's root to a specific leaf needs to be downloaded. This results in path lengths of log N. A naive idea to reduce path lengths would be to percolate elements to any node in the tree, not only leaves, but also interior nodes. To cope with added elements destined to interior nodes, the size of nodes, i. e. , the number of elements that can be stored in such buckets, would need to be increased.
The number of entries (blocks) in every node varies depeding on the scheme. We apply the result of Theorem 4. 4 and Theorem 4. 6 to vary the size of the buckets accordingly. For the poly-logarythmic client memory, the size of the buckets of r-ORAM over Path ORAM are set to z = 6 based on Theorem 4. 8. We take communication and storage overhead of the position map into account as well as the overhead induced by eviction (eviction rate equal to 2 for the constant client memory case). Figure  5  depicts the communication cost per access, i. e. , the number of bits transmitted between the client and the server for any read or write operation. The graph shows that r-ORAM applied to Path ORAM (z = 6) gives the smallest communication overhead. For example, with a dataset of 1 GByte, an access will cost 100 KByte in total.
Experimental Results For a real-world comparison, we have implemented Path ORAM and r-ORAM including the position map in Python. Our source code is available for download  [25]. Experiments were performed on a 64 bit laptop with 2. 8 GHz CPU and 16 GByte RAM running Fedora Linux. For each graph, we have simulated 10 15 random access operations. The standard deviation of the r-ORAM height (communication complexity) was low at 0. 015. The relative standard deviation for the average height (communication complexity) for 93312≈ 2 16. 5 elements equals to 0. 125. The experiments begin with an empty ORAM. We randomly insert the corresponding number of elements. This step represents the initialization phase. Afterwards, we run multiple random accesses to analyze the height behavior and the stash size for r-ORAM over Path ORAM.
After evaluation of the abstracts, the Scientific Committee selected 29 proposals for oral presentations, and accepted 120 posters. The topics of the meeting initially were not restricted to specific areas of interest in Bioinformatics. However, after the selection of the abstracts, the Conference program was organized into the following sessions: Omics; Synthetic and Systems Biology; Algorithms for Bioinformatics; Macromolecular Structure; Stochastic Modeling; Biological Databases; Genomics, Transcriptomics and NGS; Management Integration and Analysis of Clinical and Biomolecular Data; Metagenomics; Molecular Evolution; Pharmacogenomics; Protein Structure and Function; Proteomics.
Bioinformatics Seven articles have been accepted for publication in BMC Bioinformatics. They cover different aspects of the field, from novel algorithms, application and comparison of analysis methods to specific data, tool development. Carrara et al. [3]  investigate the relationship between different Library Sample Preparation methods and bioinformatics analysis methods on the efficiency of the detection and quantification of alternative splicing from paired end datasets (50 bp). Their interesting and somewhat surprising results allow to discriminate among protocols for alternative splicing detection. The analyses performed with transcript-focused and exon-focused methods show comparable results, with a better performance of exon-level methods for higher sequence depth, as measured by spikeins.
Moreover, the lower sensitivity in detection of isoforms is associated with the rRNA depletion as opposed to PolyA + selection. This would be due to the sequence reads used for non-coding RNA (supposedly long ncRNAs) coverage, which would be under represented in PolyA + libraries. Finally, all the test datasets are made available to the community, including the spike-ins. This will be important for method developers. Supervised classification is a well-known problem in statistical learning. From one hand, there is a great amount of methods that perform the classification as "black-box" (i. e. , without providing a set of intelligible rules explaining how the classification is carried out). On the other hand, there are relatively few approaches that provide intelligible "AND-type" and "OR-type" rules.
Calabria et al. [6]  describe the development of a LIMS system, named adLIMS, which tracks cell samples collected by clinicians as soon as they are processed, using PCR and Next Generation Sequencing methods, to produce data for research projects and clinical trials. The system was developed by modifying an existing open source software, ADempiere ERP, in order to satisfy the necessary user requirements. Atomistic molecular dynamics simulations with explicit solvent, coupled to enhanced sampling techniques and advanced analysis methods, allow to study at molecular level several fundamental biological phenomena, such as the regulation of gene expression. In the paper by Di Palma et al. [7]  these approaches are used to study the structural mechanism regulating the conformational changes of bacterial riboswitches upon binding of sensed ligands, suggesting a molecular explanation of these phenomena.