dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description Participating teams (Table  1 ) had four months in which to assemble genome sequences from a variety of NGS sequence data (Table  2 ; Additional file 1) that was made available via the Assemblathon website  [29]. Each team was allowed to submit one competitive entry for each of the three species (bird, fish, and snake). Additionally, teams were allowed to submit a number of 'evaluation' assemblies for each species. These would be analyzed in the same way as competitive entries, but would not be eligible to be declared as 'winning' entries. Results from the small number of evaluation entries  (3, 4  and 0 for bird, fish, and snake respectively) are mostly excluded from the Analyses sections below, but are referenced in the Discussion. Assemblies were generated using a wide variety of software (Table  1 ), with greatly varying hardware and time requirements.
The submitted assemblies for Assemblathon 2 are available from the Assemblathon website [29] and also from GigaDB  [30]. All input reads have been deposited in sequence read archives under the accessions ERP002324 (bird), SRA026860 (fish), and ERP002294 (snake); see Additional file 9 for a detailed list of all associated sequence accessions. Details of the bird sequence data, as well as gene annotations, have also been described separately  [31]. The assembled Fosmid sequences for bird and snake that were used to help validate assemblies are also available in GigaDB  [32]. Source code for scripts used in the analysis are available from a Github repository  [33]. Results for all of the different assembly statistics are available as a spreadsheet (Additional file 4) or as a CSV text file (Additional file 5).
Their competitive assembly ranked 2nd overall, and was produced by the metassembler tool [55] which combined the results of two separate assemblies (CSHL* and CSHL**, that were made using the Allpaths and SOAPdenovo assemblers respectively). Both of these CSHL evaluation assemblies were produced using the default parameters for the assembly software in question (though for the SOAPdenovo assembly, CSHL team also used Quake  [56]  to error correct the reads before assembly). The CSHL Allpaths assembly ranked slightly higher than the competitive entry from the Allpaths team, though this is only apparent in the z-score rankings (they produce the same average rank, Figure  S10  in Additional file 2). In contrast, the CSHL SOAPdenovo entry ranked much lower than the evaluation assembly from the SOAPdenovo team entry (SOAP*).
By many metrics, the best assemblies that were produced were for the snake, a species that had a larger genome than the other two species, but which had fewer repeats than the bird genome (as assessed by RepeatMasker analysis). The snake dataset also had the lowest read coverage of all three species, with less than half the coverage of the bird (Table  2 ). Higher levels of heterozygosity in the two other genomes are likely to be responsible for these differences. We used ten 'key metrics' which each capture a slightly different facet of assembly quality. It is apparent that using a slightly different set of metrics could have produced a very different ranking for many of the assemblies (Figures  17 18 19 , Figures S9-S11 in Additional file 2). Two of these key metrics are based on alignments of scaffolds to optical maps and these metrics sometimes revealed very different pictures of assembly quality.
As in Assemblathon 1  [28] , we find that N50 remains highly correlated with our overall rankings (Figure  20 ). However, it may be misleading to rely solely on this metric when assessing an assembly's quality. E. g. the SOAP bird assembly has the 2nd highest N50 length but ranked 6th among competitive assemblies based on the overall z-score. Conversely, assemblies with low scaffold N50 lengths may excel in one or more specific measures of assembly quality. E. g. the Ray snake assembly ranked 9th for N50 scaffold length but ranked 1st in the two COMPASS metrics of coverage and validity. Recently, another assembly quality metric has been proposed that uses alignments of pairedend and mate-pair reads to an assembly to generate Feature-Response Curves (FRC)  [15, 64]. This approach attempts to capture a trade-off between accuracy and continuity, and has recently been used to assess a number of publicly available genome assembly datasets including the snake assemblies that were submitted for Assemblathon 2  [65].
Three fish assemblies (CSHL, CSHL*, and SOAP*) were all found to contain the most core genes (436 out of 458 CEGs), but 455 CEGs were present across all assemblies. Combining assemblies is the approach that the GAM team used for their snake assembly. The GAM program (Genomic Assemblies Merger)  [67]  combined separate assemblies produced by the CLC and ABySS assemblers  [8, 68]. However, the resulting assembly scored poorly in most metrics. In contrast, the metassembler entry from the CSHL team produced a high-ranking assembly, but one that was only marginally better than the two source assemblies that it was based on. One important limitation of this study is that we did not assess the degree to which different assemblers resolved heterozygous regions of the genome into separate haplotypes.
Calculating average vertebrate gene length Using the Ensembl 68 Genes dataset  [70] , we extracted the latest protein-coding annotations for human (Homo sapiens), chicken (Gallus gallus), zebrafish (Danio rerio), a frog (Xenopus laevis), and a lizard (Anolis carolinensis). From these datasets, we calculated the size of an average vertebrate gene to be 25 Kbp.
Fosmid analysis Repeats in Fosmid sequences were identified using version open-3. 3. 0 (RMLib 20110920) of the online RepeatMasker software  [74]. Reads were aligned to Fosmids using BLASTN with parameters tuned for shorter alignments with some errors (WU-BLASTN [04-May-2006] W=11 M=1 N=-1 Q=2 R=2 kap S=50 S2=50 gapS2=50 filter=dust B=1000000 V=1000000). For snake, 8 lanes of short-read Illumina sequence were used (flowcell ID: 110210_EAS56_0249_FC62W0CAAXX). For bird, we used the 5 lanes of short-insert Illumina data from Duke University (see Supplementary Material for details). Finally, Fosmid sequences were aligned to assembly scaffold sequences using BLASTN with parameters tuned for long, nearly identical alignments (WU-BLASTN [04-May-2006] W=13 M=1 N=-3 Q=3 R=3 kap S=1000 S2=1000).
VFR COMPASS analysis COMPASS  [41]  is a Perl script that uses Lastz  [58]  to align assembly scaffolds to a reference, after which the alignment is parsed (using SAMTools [75]) to calculate alignment and coverage island lengths (see Figure  7 ), which are used to create cumulative length plots (e. g. Figure  10 ), as well as to calculate coverage, validity, multiplicity, and parsimony metrics. COMPASS was run with a minimum contig length of 200 bp for all submitted assemblies, and with the following lastz command: lastz reference[multiple] assembly[multiple] --ambiguous=n --ambiguous=iupac \ --notransition --step=20 --match=1,5 --chain --identity=98 --format=sam > out. sam Note the options specifying a minimum 98% identity cutoff for alignments and treatment of 'N' characters as ambiguous (receiving scores of zero, rather than a penalty for mismatch); these and other options may not be appropriate for all cases.
VFR distance analysis A Perl script (vfr_blast. pl [33] ) was used to loop over successive 1,000 nt regions from the VFR sequences for bird and snake. Pairs of 100 nt 'tag' sequences were then extracted from the ends of each of these regions. All pairs of tag sequences were then searched against all scaffolds for that particular species using BLAST  [76]. Matches were only retained if at least 95 nt of each tag sequence aligned to the scaffolds. The resulting BLAST output was processed to determine whether both tag sequences from a pair, matched uniquely to a single scaffold, and if so, at how far apart (expected distance between start coordinates of each tag in a pair is 900 nt).
2) Abstract Assemblathon 2 is a genome assembly contest where participating teams attempted to assemble genomes for three vertebrate species using a mixture of next-generation sequencing data. In total, 43 assemblies were submitted for three species (15 for bird, 16 for fish, and 12 for snake). These assemblies were assessed using a wide variety of statistical approaches as well as using experimental data from Fosmid sequences and optical maps. 3) Author list: as in publication. 4) Data types: Genome assemblies (gzipped FASTA files) 5) Organism or tissue for each data type: Budgerigar (Melopsittacus undulatus), a Lake Malawi cichlid (Maylandia zebra, also referred to as Metriaclima zebra), and boa constrictor (Boa constrictor constrictor). 6) Dataset size: 27 GB (86 compressed *. fa. gz files, and one MD5 checksum file).
K Bradnam helped organize the contest and liaised with various groups to facilitate participation; coordinated submission of entries; made anonymous versions of assemblies available to all third-party groups performing evaluations; helped design and coordinate experiments; calculated and collated all basic statistics for each assembly; ran CEGMA against all assemblies; collected datasets to calculate 'average size of vertebrate gene'; looked for available transcript data; performed distance analysis on VFR sequences; integrated all analyses from other groups; herded goats; performed z-score analyses of assemblies; made heat map from final rankings; drafted, wrote, and edited the manuscript.
Acknowledgements The Assemblathon 2 organizers would like to thank all groups and people involved in generating the datasets used for this project. Specifically we thank: •  BGI, Roche 454, Pacific Biosciences , and  Erich Jarvis  ( NIH  pioneer award and HHMI funds) for providing the budgerigar (Melopsittacus undulatus) sequence data. •  NHGRI  for providing funds to sequence Fosmids and produce optical maps. • The  Broad Institute Genomics Platform  and  Genome Sequencing  and  Analysis Program ,  Federica Di Palma , and  Kerstin Lindblad-Toh  for making the sequence data for the Lake Malawi cichlid (Metriaclima zebra) available; and  Thomas Kocher  at the  University of Maryland  for providing blood samples for the cichlid. • Illumina (and  Ole Schulz-Trieglaff  in particular) for providing the red-tailed boa constrictor (Boa constrictor constrictor) sequence data,  Matthew M.
Hims  and Niall A. Gormley for preparing and QC-ing the Illumina mate-pair libraries, and  Freeland Dunker  at the  California Academy of Sciences  for taking the snake blood samples. We would also like to thank the assistance of  Amazon Web Services (AWS)  and  Pittsburgh Supercomputing Center (PSC)  for help with the initial distribution of Assemblathon 2 datasets. We also thank the help and support of the  UC Davis Bioinformatics Core  in providing resources to help finish the analysis of the data.
Acknowledgements from Allpaths team: This work has been funded in part by the  National Human Genome Research Institute, National Institutes of Health, Department of Health and Human Services , under grants  R01HG003474  and  U54HG003067 , and in part by the  National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services , under Contract No. HHSN272200900018C.
Acknowledgements from BCM-HGSC team: We gratefully acknowledge funding support from a grant  U54 HG003273  to RAG from the  National Human Genome Research Institute, National Institutes of Health ; computational resources provided by  National Center For Research Resources, National Institute of Health  through a grant to  Jeffrey Gordon Reid  ( 1 S10 RR026605 ) for a massive RAM computer cluster at BCM-HGSC; access to the IBM POWER 7 bioscience computing core BlueBioU machine at  Rice University ; and an  IBM   Faculty Award  to KCW. We thank  Adam English  for mapping the PacBio budgerigar data to the preliminary budgerigar assembly.
Acknowledgements from CRACS team: The work has been partially supported by  European Community 's  FP7/2011-2015, EurocanPlatform  under grant agreement no  260791 ,  PTDC/EIA-EIA/121686/2010 , and funds granted to  CRACS & INESC Tec LA  through the  Programa de Financiamento Plurianual, Fundacao para a Ciencia e Tecnologia and Programa POSI. Acknowledgements from CSHL team:  National Institutes of Health  award ( R01-HG006677-12 ), and  National Science Foundation  award ( IIS-0844494 ) to MCS Acknowledgement for MLK team:  Matthew MacManes  was supported by a  National Institutes of Health   NRSA Post-Doctoral fellowship  ( 1F32DK093227 ). Computational resources were supported by the  Extreme Science and Engineering Discovery Environment (XSEDE) PSC-Blacklight , which is supported by  National Science Foundation  grant number  OCI-1053575  Acknowledgements from Meraculous team: Work conducted at the  US Department of Energy Joint Genome Institute  is supported by the  Office of Science of the US Department of Energy  under contract numbers  DE-AC02-05CH11231.
Acknowledgements from Ray team: Computations were performed on the Colosse supercomputer at  Université Laval  and the  Guillimin supercomputer at McGill University  (nne-790-ab), under the auspices of  Calcul Québec  and  Compute Canada. The operations of Guillimin and Colosse are funded by the  Canada Foundation for Innovation (CFI) , the  National Science and Engineering Research Council (NSERC) ,  NanoQuébec , and the  Fonds Québécois de Recherche sur la Nature et les Technologies (FQRNT). JC is the  Canada Research Chair in Medical Genomics. SB is recipient of a  Frederick Banting and Charles Best Canada Graduate Scholarship Doctoral Award  ( 200910GSD-226209-172830 ) from the  Canadian Institutes for Health Research (CIHR). FL was supported in part by the  Natural Sciences and Engineering Research Council of Canada  (grant  262067 ).
Acknowledgements from SOAPdenovo team: We would like to thank the users of SOAPdenovo who tested the program, reported bugs, and proposed improvements to make it more powerful and user-friendly. Thanks to TianHe research and development team of  National University of Defense Technology  to have tested, optimized and deployed the software on TianHe series supercomputers. The project was supported by the  State Key Development Program for Basic Research of China-973 Program  ( 2011CB809203 );  National High Technology Research and Development Program of China-863 program  ( 2012AA02A201 ); the  National Natural Science Foundation of China  ( 90612019 ); the  Shenzhen Key Laboratory of Trans-omics Biotechnologies  ( CXB201108250096A ); and the  Shenzhen Municipal Government of China  ( JC201005260191A  and  CXB201108250096A ).
K Bradnam helped organize the contest and liaised with various groups to facilitate participation; coordinated submission of entries; made anonymous versions of assemblies available to all third-party groups performing evaluations; helped design and coordinate experiments; calculated and collated all basic statistics for each assembly; ran CEGMA against all assemblies; collected datasets to calculate 'average size of vertebrate gene'; looked for available transcript data; performed distance analysis on VFR sequences; integrated all analyses from other groups; herded goats; performed z-score analyses of assemblies; made heat map from final rankings; drafted, wrote, and edited the manuscript. In each case, 10 pools were sequenced at various different pooling levels, from mostly non- overlapping sets of Fosmids (1, 1, 1, 1, 2, 4, 8, 16, 32, and 48 -96 or 114 clones sequenced in total), generating Illumina 100 x 100 bp paired end sequences.
After adapter and quality trimming using Scythe and Sickle, Fosmid reads were aligned using BWA (ver. 0. 5. 9rc1-2) to the cloning vector for removal of vector-contaminated read pairs. The Velvet assembler (ver. 1. 1. 06) was then used to assemble pools up to 16, at k-mer lengths ranging from ~55 to ~79. 8) Link to data if public, or any related accession numbers in other repositories No links required to accession numbers in other repositories. 5)Organism or tissue for each data type: Budgerigar (Melopsittacus undulatus) and boa constrictor (Boa constrictor constrictor). 6) Dataset size: 1. 4MB 8) README file Panels of pooled Fosmid clone Illumina paired-end libraries (~35 Kbp inserts) were made from the bird and snake samples using methods described in Kitzman et al. 2010 (Haplotyperesolved genome sequencing of a Gujarati Indian individual.
In this context, we propose a method and an R program based on resampling approaches to determine the significance level for a series of several transformations (including dichotomous, Box-Cox and categorical transformations) of an explanatory variable in a Generalized Linear Model. The problem of correcting the estimation of the effect will not be examined here. First, we revisit the example proposed by Liquet and Commenges  [14]  on the relationship between cholesterol and dementia  [16]  to provide a framework for our discussion. In section 'Methods: Statistical context' , we present the statistical contexts relating to multiple testing; the model, the maximum test and the minimum p value procedure and finally the score tests are exposed. Section 'Methods: Significance level correction' presents the different methods of correction of the Type-I error.
Example: revisiting the PAQUID cohort example We revisited the example presented in the article of Liquet and Commenges  [13]  for a coding of a binary variable in a logistic regression. This example is based on the work of Bonarek et al. [16] , who studied the relationship between serum cholesterol levels and dementia. The data came from a nested case-control study of 334 elderly French subjects aged 73 and over who participated in the PAQUID cohort (37 subjects with dementia and 297 controls). The variables age, sex, level of education and wine consumption were considered as adjustment variables. The analysis focused on the influence of HDL-cholesterol(high-density lipoprotein) on the risk of dementia. Bonarek et al. [16]  first considered HDL-cholesterol as a continuous variable; then, to ease clinical interpretation, they chose to transform the HDLcholesterol into a categorical variable with four classes.
As under H 0 , the X i variable has no effect on the response variable, a new dataset is generated by permuting the X i variable in the initial dataset; 3. Generate B new datasets s * b , b = 1,. , B by repeating B times the step 2; 4. For each new dataset, apply the minimum p value procedure for the transfomation under consideration. We note p * b min the smallest p value for each new dataset. 5. The p value defined in (3) is then approximated by: p value = 1 B B b=1 I p * b min <p min , where I {•} is an indicator function. However, it is important to note that exchangeability need to be satisfied  [25] [26] [27] [28] [29] [30]. This condition is much more restrictive than it appears at first sight. In fact, Commenges  [29]  and Commenges and Liquet  [25]  showed that the permutation test approach for the score test is robust if the model has only one intercept under the null hypothesis, or if X i are independent of Z i for all i in the context of a linear model and the proportional hazards model.
For example, for a logistic model (where a(φ) = 1, b(θ i ) = log(1 + e θ i ), and μ i = E(Y i ) = e θ i /(1 + e θ i )), we generate Y * i according to: P(Y * i = 1|Z i ) = e γ Z i 1 + e γ Z i. Repeat this for all the subjects to obtain a sample noted s * = {Y * i , Z i , X i } 4. Generate B new datasets s * b , b = 1,. , B by repeating B times the step 3; 5. Apply for each new dataset, the minimum p value procedure for the transformation considered. We note p * b min the smallest p value for each new dataset. 6. Then, the p value defined in (3) is then approximated by: p value = 1 B B b=1 I p * b min <p min.
Gene expression heterogeneity is a key driver for microbial adaptation to fluctuating environmental conditions, cell differentiation and the evolution of species. This phenomenon has therefore enormous implications, not only for life in general, but also for biotechnological applications where unwanted subpopulations of non-producing cells can emerge in large-scale fermentations. Only time-lapse fluorescence microscopy allows real-time measurements of gene expression heterogeneity. A major limitation in the analysis of time-lapse microscopy data is the lack of fast, cost-effective, open, simple and adaptable protocols. Here we describe TLM-Quant, a semi-automatic pipeline for the analysis of time-lapse fluorescence microscopy data that enables the user to visualize and quantify gene expression heterogeneity.
Overall promoter activity and expression of the gene of interest can then be determined by fluorescence readings of culture samples. This is achieved in real time using suitable plate reader assays  [5] [6] [7]. To investigate gene expression heterogeneity in different cells of growing populations, alternative approaches are needed, such as flow cytometry and time-lapse microscopy. Only time-lapse microscopy allows real-time measurements, and this technique is substantially less laborious than flow cytometry. Different time-lapse microscopy set-ups have been described in the recent literature  [8] [9] [10]. Though very effective, a significant drawback of these approaches is that the downstream data analysis usually requires expensive, highly sophisticated, and/or custom-made software  [9] [10] [11] [12].
Since we needed a simple and readily adaptable tool for the quantitative analysis of large amounts of time-lapse microscopy data, we established the TLM-Quant pipeline for data processing and analyses based on open-source software. This pipeline was validated using a custom-built fluorescence microscopy set-up and B. subtilis strains producing GFP from promoters that direct either homogenous, heterogeneous, or bistable gene expression, as described by Botella et al. [6]. Importantly, the TLM-Quant pipeline was then effectively implemented in a large-scale systems biological analysis on the global network reorganization during dynamic adaptations of B. subtilis metabolism to nutritional shifts between the preferred carbon sources glucose and malate  [7]. In the latter study, TLM-Quant allowed us to verify the absence of heterogeneity in the expression of genes involved in central carbon metabolism.
The respective datasets can be queried at https://basysbio. ethz. ch/ openbis/index. html?viewMode = SIM-PLE#action = DOWNLOAD_ATTACHMENT&file = populatio-opulation+homogeneity. pdf&&entity = PROJECT&code = BASYS-BIO_BIG&space = BASYSBIO_PUBLIC or http://tinyurl. com/ basysbiodata. A detailed description of TLM-Quant as presented here and in the Tutorial S1 was however not published thus far.
Analysis For image analysis by TLM-Quant, we will assume that, for each time point, a phase-contrast image and an overlapping fluorescent image are available, both encoded in 8-bits (intensity from 0 to 255). Downstream processing can be generalized to multiple channels (colours). To visualise and quantify gene expression heterogeneity, the fluorescence information in the recorded images is extracted using ImageJ software (available via http://rsbweb. nih. gov/ij/) [12]. To obtain correct cellular fluorescence measurements, cells are segmented in phase contrast images by using the commands 'Subtract background' and 'Convolve'. The kernel used in the 'Convolve' command is specified in Figure  1A  and should be adjusted depending on cell type and exposure time. A copy of the obtained image is converted to a binary mask (intensity 0 or 255) using the 'apply' command in the threshold dialogue.