dataesr/scientific-paragraphs-categorization huggingface dataset:
Program interface The MafFilter program provides a command line interface to the MAF parser and filters from the Bio++ libraries. It uses the Bio++ Options language, a 'key-value' syntax introduced for the Bio++ Program Suite which features strong similarities with the R statistical language  [5, 6]. Briefly, the behaviour of MafFilter is controlled via commands of the type option=value, which can be passed as arguments to the program, or stored in an independent option file. In the simplest case, values are simple numbers or expressions, but can be of more complex structures with arguments. Figure  3  shows the option file generating the pipeline displayed in Figure  2. The first lines describe the input file (path, format and compression). Subsequently is given a list of filters, defining the various steps in the pipeline (see Table  1  for available filters).
Code design The central data elements in a genome alignment are synteny blocks, i. e. contiguous genomic regions sharing common ancestry represented as a sequence alignment. A genome alignment consists of a collection of these blocks together with the corresponding coordinates for each single genome. We developed new data structures for handling such data. Each synteny block is stored as a MafBlock instance which stores the underlying alignment into a SiteContainer, a central class of the Bio++ library for which numerous methods and tools are already available  [7]. Individual sequences are stored as MafSequence objects, an extension of the SequenceWithAnnotation class from the bpp-seq library allowing the storage and processing of associated quality scores. In addition, MafSequence stores genomic coordinates as chromosome names, strands and start positions.
Computer efficiency As MAF files can be rather large (typically several gigabytes) MafFilter can read and write compressed files, using the zip, gzip and bzip2 compression formats. The compression and decompression is achieved with the boost-iostream library. Practically, the use of compressed files has very little impact on the memory usage or computation speed while reducing considerably the amount of disk space. At the time of writing, the amount of publicly available parsers for MAF files is rather limited. The corresponding classes in the Python language have not yet integrated the stable branch of the BioPython libraries. In order to assess the performance of the Bio++ parser, we therefore compare it to the BioPerl library. The resulting perl script (see Additional file 1) parses the compressed MAF file and outputs for each alignment block with more than a thousand sites the number of sequences, the length of the alignment and the coordinates of the sequence of one species if represented in the alignment block.
This simple pipeline allows to directly compare the efficiency of the parsers themselves, as the only computations required are file reading, as well as allocation and initialization of the dedicated structures for storing data into memory. The corresponding MafFilter option file is provided in the example directory of the distributed source code. To compare the two approaches, we used the 46 vertebrates alignment of Human chromosome 22 downloaded from UCSC  [8]  as input data, and ran the analyses on a linux.
CountClusters CountClustersMafStatistics Compute the number of haplotype groups, given a certain mutation threshold, providing a tree has been previously computed. workstation (Intel(R) Xeon(R) CPU E5520 @ 2. 27GHz, with 16Gb of RAM running Ubuntu 12. 04). The complete parsing takes 30 minutes with the BioPerl script while it completes in only 3 minutes with MafFilter. Maf-Filter was used to analyse the complete Gorilla genome aligned with other Primates (2Gb alignment)  [9] , as well as resequencing data of 27 individual genomes from the fungus Zymoseptoria pseudotritici (40Mb alignment, E. Stukenbrock, pers. communication).
Approximate the ancestral recombination graph with 1kb windows We downloaded the 46 vertebrate genome alignment from UCSC  [8]  and built a pipeline in order to infer underlying sequence genealogy along the genome (Figure  2 ). The tasks performed by MafFilter involved: (1) extracting the Primate sequences (Human, Chimp, Gorilla, Orangutan and Macaque), (2) merging syntenic blocks in Primates, (3) removing gap positions in each block, (4) cutting the resulting alignment into windows of 1kb with no synteny break and (5) computing a distance tree with Kimura distance  [10]  in each window. The analysis of chromosome 1, the largest alignment, completed in 30 minutes. The memory consumption increased at the start of the program execution, and was stable during the whole filtering, reaching a maximum value of 4,850kB (as measured by the maximum resident set size, see Additional file 2).
Extract all non-coding regions from a single genome In this example, a Fasta file containing all chromosome sequences of the fungus Ustilago maydis  [12, 13]  was imported and compared to a GFF3 annotation file containing all protein coding genes  [14]. Fasta import is enabled in MafFilter providing that the sequences are comprehensive, that is, all contig data start at nucleotide position 1. In this pipeline all gene sequences defined in the GFF files were removed. The resulting blocks were then filtered to keep only regions greater than 300 bp, and were written in a gzip-compressed MAF file. Statistics (block length, nucleotide composition) were also computed on the resulting blocks. The program completed in 18 minutes, used a maximum of 86MB of memory (See Additional file 2) and output 5,487 blocks of intergenic regions.
Conclusion Integrated analysis of large genome alignments is a computational challenge for today's comparative and evolutionary genomics research and its importance is expected to grow in the near future. We have introduced here the MafFilter program that allows the easy and efficient analysis of such data. The program is highly parametrizable and allows to perform a broad range of analyses and data processing on MAF files. In addition, the components of the underlying parsers and methods are available as  an object-oriented library, facilitating the implementation and integration of new analysis tools. As it reads and outputs standard formats, the MafFilter software is a powerful complement of existing genomic tools such as the SAMTools  [16]  and VCFTools  [17].
Consequently, a solution can be found through the use of multiscales modeling and corresponding numerical simulations of such processes  [3, 4]. Therefore, we are presently interested in understanding theoretically the mechanisms of those phenomena, especially the behaviors in the substrate during the first stage of the splitting process  [5, 6]. In this paper, we studied particularly different geometrical parameters and different initial conditions imposed on hydrogen penny-shape microcracks. We implemented this model in finite element analysis software (Abaqus) to calculate the concentration fields of hydrogen. Finally, a weakly coupled model (taking into account simultaneously diffusion and mechanical effects and its coupling) is assembled to simulate the phenomena during the first stage of the splitting process.
On the external surface, the boundary condition is homogeneous, whatever the quantity (concentration or displacement). On the internal surface (corresponding to the gas/silicon interface), because of the diffusion phenomena, the boundary conditions are not a priori homogeneous. By using Eq. 7, after a first step, the pressure P due to hydrogen within the penny-shape microcracks is calculated. All the relations (3) and (  7 ) have been implemented into Abaqus® FEM software using the subroutine VUINTER. The lateral surface S(t) and volume V(t) of this penny-shape microcracks are evaluated using the geometry evolution at each step. Then a calculation of the average pressure is done using: ∑ ∑ = = = n i i n i i i S S P P 1 1 /. ( 8 ) where "i" means for the ith node around the penny-shape microcracks.
45 atom/nm 3. Numerical simulations. The simulations have been performed with Abaqus FEM software. Because it does not exist any predefined unit system in Abaqus, we have customized an adapted unit system, before launching the calculations. By considering the different natures of simulations, we defined the type of mesh element as linear standard (CPE3RT triangular strain plane element of Abaqus) for the simulation without coupling and as linear explicit coupled concentrationdisplacement for the simulation of coupled phenomena. Due to the irregular shape of the geometries, we choose free structured triangle elements for both simulations and set 10 seconds for both simulation step times. Results without coupling. By considering only the diffusional models defined by Eq. 5 and Eq. 6 with initial and boundary conditions, the results of the corresponding simulations are presented in Fig.
Advances in genome sequencing have made proteomic experiments more successful than ever. However, not all entries in a sequence database are of equal quality. Genome sequences are contaminated more frequently than is admitted. Contamination impacts homologybased proteomic, proteogenomic, and metaproteomic results. We highlight two examples in the National Center for Biotechnology Information non-redundant database (NCBInr) that are likely contaminated: the bacterium Enterococcus gallinarum EGD-AAK12 and the insect Ceratitis capitata. We hope to incite users of this and other databases to critically evaluate submitted sequences and to contribute to the overall quality of the database by signaling potential errors when possible.
sulfurreducens strains, despite the use of standard microbiological techniques to maintain pure cultures  [6]. When contaminant sequences are not removed, they confound taxonomic identification in metagenomic samples using techniques such as fragment recruitment  [7]  and they translate to improperly classified protein sequences in databases such as the widely used non-redundant (nr) protein database maintained by the National Center for Biotechnology Information (NCBI). Genome sequence quality control measures exist, but their use is not universal. Standard parameters for sequence quality control include GC content, the expected genome length, the number of reads, and the N50  [8]. Additional algorithms, such as Kontaminant, can look for overlap between newly generated sequences and specified reference contaminants  [9].
Thus, a myriad of criteria and automated programs exist to assist in the proof-reading of genome sequences, but their use is neither mandatory nor consistent. The burden of quality control for sequences in public repositories is generally placed upon those submitting the sequences, but information regarding what measures have been taken, if any, is not transmitted to database end-users. The NCBI Reference Sequence (RefSeq) collection is a manually curated subset of the NCBInr database; however much of the additional standards are focused on genome annotation (see inset), and again a range of tools exist  [10]. Furthermore, the status of microbial genomes in the RefSeq database is typically "provisional," meaning, "The RefSeq record has not yet been subject to individual review. The initial sequence-to-gene association has been established by outside collaborators or NCBI staff"  [10].
The Universal Protein Resource Knowledgebase (UniProtKB) is similarly based on nucleotide submissions to databases like the NCBI's GenBank  [11]. Like RefSeq, entries in the SwissProt collection of UniProt, as opposed to TrEMBL, which is not curated, are further subject to extensive manual curation, again generally focused on annotation. Results of the 2010 NCBI workshop on genome annotation  [12]  1. MINIMAL GENOME ANNOTATION SHOULD HAVE a. rRNAs (5S, 16S, 23S) and corresponding genes with locus tags, b. tRNAs and corresponding genes with locus tags, c. protein-coding genes with locus tags (see below) and corresponding CDS. A non-exhaustive catalog of reliable software, sources, and databases for the production of microbial genome annotation is both a useful community resource that aids in producing high quality genome annotation.
VALIDATION CHECKS AND ANNOTATION MEASURES. Validation checks should be done prior to the submission. NCBI has already provided numerous tools to validate and ensure correctness of annotation. Additional checks will be put in place to ensure the minimal standards are met. With all of the various quality control standards and methods for meeting them, it is difficult for end-users to evaluate the trustworthiness of database entries, so by default most sequences are given the benefit of the doubt. In the absence of proof of error, most sequences are assumed to be correctly assembled and annotated and attributed to the correct taxonomic group. However, during the course of an analysis, it sometimes becomes obvious that something is amiss. Notable recent cases include the mis-annotation of a keratin-derived sequence as a plant protein  [13]  and the mistaken inclusion of bacterial sequences in the genomic sequences of two Caenorhabditis species  [14].
A string of such incidents inspired us to perform a cursory survey of the NCBInr database, revealing a glimpse at the extent to which contamination is present. We present the following BLAST alignments as evidence of sequence contamination. The first taxon that we identified as suspicious was the bacterium Enterococcus gallinarum EGD-AAK12 (taxid: 1357296), which supposedly contains 12,518 genes, 12,300 of which are annotated as coding sequences (CDS). This 11 Mbp genome of this Firmicute of the order Lactobacillale is in the "scaffolds or contigs" status, with 6194 contigs and a contig N50 at 3468 nt. Its proteome is available in the NCBInr but is not included in the RefSeq protein database. Because of the discrepancy between the genome size of this strain and those of other Enterococcus bacteria (generally approximately 3 Mbp), we examined this organism using BLAST queries.
We created a proteome fasta file (ORGfasta) using an http request in the form http://www. ncbi. nlm. nih. gov/protein/?term=txid1357296 [Organism:noexp] (for taxid: 1357296) and post-processed using a Python script to only retain non-redundant sequences. To identify which sequences could be related to a contamination of the proteome by a different organism, the ORGfasta proteome was processed as follows: (i) a first BLASTp search was performed using a small subset of ORGfasta against the NCBInr database at an evalue threshold of 1E-20 to identify the contaminating genus (CONTgenus), (ii) a list of GIs associated to CONTgenus children taxa was compiled using the NCBI taxonomy gi taxid prot. dmp file, (iii) fasta entries corresponding to this GI list were retrieved from NCBInr to build a BLAST database (CONTgenus BLASTdb), (iv) a BLASTp search of the ORGfasta proteome against the CONTgenus BLASTdb was performed, and (v) the output BLAST xml was parsed using a Python script to list ORGfasta sequences with hits at an evalue below 1E-20 for sequences of the exact same length on any CONTgenus sequence.
A reference proteome (REFfasta) created using a strain of the same genus whose genome is complete, Enterococcus faecium DO, was submitted to the same BLAST search against the CONTgenus BLASTdb. An Enterococcus BLAST database excluding both the ORGfasta and REFfasta sequences was built, and the ORGfasta and REFfasta sequences were searched against this database, again using BLAST (Table  1 ). Of the 12,300 predicted E. gallinarum EGD-AAK12 proteins, 3198 were almost identical to a Klebsiella sequence (same length, BLAST evalue <1E-20), whereas for Enterococcus faecium DO, the closest "complete" reference organism, only 103 sequences met these criteria, giving an estimate for the number of false positives. Thus, at least one Klebsiella strain was likely present as a contaminant in the DNA used for sequencing.
Only 244 E. gallinarum EGD-AAK12 sequences are almost identical to an Enterococcus sequence (excluding ORGfasta and E. faecium sequences), whereas E. faecium DO yielded 4122 hits. The high representation of species close to E. faecium in the Enterococcus database could explain the number of hits for E. faecium DO, but the low number of hits for E. gallinarum EGD-AAK12 raises questions. Because of the presence of Klebsiella, a Proteobacteria of the order Enterobacteriales, in the sample and because of the generation of short reads in the Illumina sequencing approach, assembly errors could lead to erroneous chimeric protein sequences in the output, reducing the number of correctly assembled coding sequences and thus reducing number of sequences shared with other enterococci. The second dubious taxon that was selected for investigation was Ceratitis capitata (taxid: 7213), which is a eukaryote from the order Diptera, well-known for causing extensive damage to fruit crops.
This organism was examined as for the first example, and the results are given in Table  2. Another well-known Diptera, namely Drosophila melanogaster (taxid: 7227, Bioproject 164), was used as the reference proteome. The C. capitata genome is in the "scaffolds or contigs" status, and its proteome is available both in the NCBInr and RefSeq protein databases. We detected 789 C. capitata proteins that were almost identical to sequences from the Escherichia genus (same length, BLAST evalue <1E-20). In comparison, only nine such matches were obtained from the 21,402 non-redundant D. melanogaster sequences. As in the previous example, the number of BLAST hits on a Diptera order database excluding sequences from both proteomes is much higher for D. melanogaster than for C. capitata, which strengthens the hypothesis that taxid: 7213 is contaminated with Escherichia sequences.
capitata sample, masking the contamination effect. These examples serve as reminders that researchers need to be vigilant when it comes to using sequence databases. Proteogenomics has been proposed as a routine procedure to augment genome annotation using empirical evidence for valid sequences  [16] , and proteogenomics studies should be performed for any novel organism belonging to a poorly characterized branch of the Tree of Life. The use of extended databases such as the NCBInr for metaproteomics, comparative proteogenomics, and homology-based proteomics  [17]  makes it even more crucial to improve the accuracy of this sequence database. Whether discovered by discrepancies in the GC content, taxonomy, or BLAST results, contamination is a real problem that can be identified at many stages, including in the course of proteomic studies that occur long after the sequences have been completed and deposited in the database of choice.
In such cases, database end-users have the responsibility to indicate those discrepancies. Databases, especially RefSeq and UniProt, which require a large manual curation component  [18]  welcome outside contributions to the effort to maintain sequences of the highest quality and correctness. Potential errors in RefSeq can be signaled through their website at http://www. ncbi. nlm. nih. gov/projects/RefSeq/update. cgi. Similarly, UniProt welcomes suggestions either through their website at http://www. uniprot. org/contact or by e-mail addressed to help@uniprot. org. While manual curation is a valiant effort, new automated tools are needed to survey existing databases for deviations in quality.
Accountability is becoming increasingly required in today's distributed systems. Indeed, accountability allows not only to detect faults but also to build provable evidence about the misbehaving participants of a distributed system. There exists a number of solutions to enforce accountability in distributed systems, among which PeerReview is the only solution that is not specific to a given application and that does not rely on any special hardware. However, this protocol is not resilient to selfish nodes, i. e. nodes that aim at maximising their benefit without contributing their fair share to the system. Our objective in this paper is to provide a software solution to enforce accountability on any underlying application in presence of selfish nodes. To tackle this problem, we propose the FullReview protocol.
g. , anonymous communication  [7] , online games  [26] , network storage  [27] , randomised systems  [2] , inter domain routing  [16] , virtualised systems  [17] ). While these solutions offer strong accountability guarantees, their usability is limited to the specific application domain for which they have been devised. Hence, generic solutions that are not tailored to a specific application have been proposed, some of which rely on trusted hardware (e. g. , Trinc  [21] , A2M  [6] , Pasture  [19] ) while others are generic software solutions. Our work targets this second category of systems as they do not require users (worldwide) to acquire specific hardware. To the best of our knowledge, PeerReview  [15]  is the only protocol that falls into this category of systems. In this protocol, nodes log their interactions with other nodes in a secure log.
While dealing with Byzantine nodes is important, it has been demonstrated that in open collaborative environments selfish nodes, also called free riders, constitute a real threat  [10] ,  [20] ,  [12] ,  [9]. Selfish nodes are nodes that tamper with their software (or download a tampered software developed by others) in order to benefit from the system without contributing their fair share to it. In PeerReview, nodes are not encouraged to participate to the monitoring of other nodes, which makes it vulnerable to selfish nodes. Specifically, in presence of a proportion of selfish nodes, some nodes in the system can be unsupervised if all their monitors behave selfishly. As a result, these nodes can harm the system without being detected, breaking the completeness property of PeerReview. To measure the impact of this threat in practice, we deployed PeerReview for enforcing accountability in the following two protocols: SplitStream  [5] , an efficient multicast protocol and Onion routing  [13] , the most used anonymous communication protocol.
This veliiicalion ensures that i's log contains a sequencing of messages that reflect a correct behaviour. 1--ior instance, a correct log should contain periodic requests from i to the set of nodes it monitors. i. e. the nodes in m -1 (i). The ahsence oJ such peliodic messages re1lects a faulty behaviour. We explain how these verifications are performed in FullRn,iew in Section V-B3. However, a log that exhihits a correct sequencing of messages is not sufficient to guarantee a correct hehaviour. Hence. the last verification that is performed hy i's monitors is lo assess whether i's Log co1Tesponds Lo a correct execution oJ the protocols l' and Al or not. Verifying the confonnance oJ i's log with a conecL execution oJ l' is performed as in the PeerReview protocol. i. e. by re -executing the code of the protocol P using a reference implementation.
VII. CONCLUSION This paper addresses the problem of accountable distributed systems in presence of selfish nodes. We have shown that PeerReview, the only software generic solution to enforce accountability, does not tolerate selfish nodes. To tackle this problem we propose the FullReview protocol. This protocol uses game theory techniques by embedding incentives that force selfish nodes to stick to the protocol. We have evaluated FullReview on a cluster of physical machines and using simulation with two applications: SplitStream, an efficient multicast protocol, and Onion routing, the most widely used anonymous communication protocol. Our evaluation makes the following points. First, contrarily to PeerReview, FullReview effectively tolerates selfish nodes. Second, FullReview has a low additional overhead compared to PeerReview.