dataesr/scientific-paragraphs-categorization huggingface dataset:
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: metagenomics, microbial ecology, phage genomics, viral ecology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. from fastq treatment (contaminant filtering, taxonomic assignment, alpha and beta diversity indexes) to statistical analysis (using three main differential analysis methods, DESeq2, Metacoder, metagenomeSeq) in an automatic, step by step, and reproducible way.
robjects. Rdata with saved dada_res list containing raw ASV table and representative sequences in objects otu. table, seqtab. export, seqtab. nochim. # Adjust sample names to match with metadata colnames(dada_res$otu. table)=tools::file_path_sans_ext(colnames(dada_res$otu. table))Taxonomic assignment. assign_taxo_fun function uses IDTAXA function from DECIPHER package, and allows to use two different databases. It keeps the best assignment on two criteria, resolution (depth in taxonomy assignment) and confidence (value givenby IDTAXA). The final taxonomy is validated by checking for multiple ancestors (i. e. same species assigned to different genus) and incongruities (i. e. empty fields or incomplete lineage) correction step.
10. 5281/zenodo. 4338833 26 icense: Creative Commons Attribution 4. 0 InternationalHere we show all packages used in this workflow and their version numbers: # [105] htmltools_0. 5. 0 # [109] tidyr_1. 1. 0 # [113] formatR_1. 7 # [117] ade4_1. 7-15 # [121] quadprog_1. 5-8 # [125] forcats_0. 5. 0 # [129] plotly_4. 9. 2. 1 # [133] admisc_0. 9 # [137] callr_3. 5. 1 # [141] Biostrings_2. 54. 0 # [145] curl_4. 3 # [149] nlme_3. 1-149 # [153] carData_3. 0-4 # [157] limma_3. 42. 2 # [161] lattice_0. 20-41 # [165] survival_3. 1-12 # [169] fdrtool_1. 2. 15 # [173] bit_1. 1-15. 2 # [177] DESeq2_1. 29. 13 # [181] dplyr_1. 0. 0 venn_1. 9 geneplotter_1. 64. 0 MASS_7. 3-53 car_3. 0-10 parallel_3. 6. 3 pkgconfig_2. 0. 3 foreach_1. 5. 0 multtest_2. 42. 0 digest_0. 6. 25 rmarkdown_2. 5 Rsamtools_2. 2. 3 jsonlite_1.
04. 5 LTS # # Matrix products: default # BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas. so. 3. 7. 1 # LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack. so. 3. 7. 1 # # locale: # [1] LC_CTYPE=fr_FR. UTF-8 LC_NUMERIC=C LC_TIME=fr_FR. UTF-8 # [5] LC_MONETARY=fr_FR. UTF-8 LC_MESSAGES=fr_FR. UTF-8 LC_PAPER=fr_FR. UTF-8 # [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=fr_FR. UTF-8 LC_IDENTIFICATION=C LC_COLLATE=fr_FR. UTF-8 LC_NAME=C # # attached base packages: # [1] stats graphics grDevices utils datasets methods base # # other attached packages: # [1] ranomaly_0. 0. 0. 9000 # # loaded via a namespace (and not attached): # [1] taxa_0. 3. 4 tidyselect_1. 1. 0 htmlwidgets_1. 5. 1 RSQLite_2. 2. 0 # [5] AnnotationDbi_1. 48. 0 grid_3. 6. 3 BiocParallel_1. 20. 1 Rtsne_0. 15 # [9] devtools_2. 3. 2 IHW_1.
Josyula et al. (2020)  shows that a mixed-integer programming (MIP) algorithm outperforms an heuristic for the five minimization objectives considered but the heuristic is faster in some specific instances. The surveys point out that integer programming (IP) and MIP models are the most popular approaches along with graph models, while CP ones are more seldom used. Nevertheless, CP models have some undeniable merits which make them interesting for this problem. In particular, they are able to generate feasible solutions for some hard problems in a short computation time. As an example, to generate the cyclic timetables of the Dutch network  (Kroon et al. , 2009) , the method of the CADANS module to solve the Periodic Event Scheduling Problem (PESP) formulation is based on CP techniques  (Schrijver and Steenbeek, 1994).
The surveys point out that integer programming (IP) and MIP models are the most popular approaches along with graph models, while CP ones are more seldom used. Nevertheless, CP models have some undeniable merits which make them interesting for this problem. In particular, they are able to generate feasible solutions for some hard problems in a short computation time. As an example, to generate the cyclic timetables of the Dutch network  (Kroon et al. , 2009) , the method of the CADANS module to solve the Periodic Event Scheduling Problem (PESP) formulation is based on CP techniques  (Schrijver and Steenbeek, 1994). We can also mention that the PESP instances of the whole inter city network of Germany and the south and east subnetworks have been solved with a SAT-solver  (Großmann et al. , 2012) , which uses specific CP techniques for variables with boolean domains.
Detecting CNVs from the intensity measurements of genotyping array probes is prone to producing unreliable calls due to the high level of noise. We recently developed a novel CNV calling method, CamCNV  14  , which focuses on rare CNVs and identi es outlier samples that may have a CNV, based on the intensity distribution across all samples at each probe. We showed that this approach is able to detect CNVs using as few as three probes  14. Here, we apply this approach to a very large array genotype dataset to search for novel breast cancer associated CNVs.
Subjects Data were derived from study participants in 66 studies participating in the Breast Cancer Association Consortium (BCAC) and genotyped as part of the OncoArray  7, 15  and iCOGS 6 collaborations (Supplementary Table  1 ). Studies included population-based and hospital-based case-control studies, and case-control studies nested within prospective cohorts; we only included data from studies that provided both cases and controls. Phenotype data were based on version 12 of the BCAC database. Cases were diagnosed with either invasive breast cancer or carcinoma-in-situ. Oestrogen receptor (ER) status was determined from medical records or tissue microarray evaluation, where available. Analyses were restricted to participants of European ancestry, as de ned by ancestry informative principal components  6, 7.
Discussion We used the largest available breast cancer case-control dataset, comprising more than 86,000 cases and 76,000 controls with array genotyping, to test for associations with rare CNVs. Using the intensities from genotyping arrays to detect CNVs is not ideal due to a high level of noise and uncertainty in the calling, particularly for duplications. However, in tests of known CNVs and replication of calls between duplicate samples, the CamCNV method shows reasonable levels of sensitivity and speci city  14. The main focus of this analysis was low frequency CNVs (< 1% frequency) since higher frequency CNVs can generally be studied through imputation to a reference panel. In the 0. 05%-1% frequency range, we could detect ~ 20% of the CNVs identi ed by the 1000 Genomes project. For some loci we only had evidence from one array because the probes do not exist to detect the variants on the other array.
001 for regions and 0. 002 for genes. Outside the known genes none of the BDFPs gave a probability below 10%, with the lowest BFDP of 0. 11 for the deletion in the FGFR2 locus. For a CNV observed with a frequency of 0. 1% (n = 91 samples in the OncoArray dataset) we had 40% power to detect an association with an odds ratio of 2 but only 1. 5% power to detect an association with an odds ratio of 1. 5. An OR of 2, comparable to that seen for deleterious variants in ATM and CHEK2, may be plausible for rare coding CNVs or non-coding CNVs that have a signi cant effect on gene expression. Larger sample sizes will clearly be required to evaluate rare CNVs with more modest associations. In addition to the BRCA1 and CHEK2 loci, we found associations in three known susceptibility regions identi ed through GWAS, harbouring FGFR2, ADCY8 and KLF12.
75 or lower (Fig. 2 ). For the 0. 4Mb duplication region on chromosome 21 (OR = 2. 23 P = 0. 0001) the probe-speci c results from OncoArray highlighted that the signal is speci c to a shorter intergenic region (21:33421860-33459975) between HUNK and LINC00159. We observed some evidence of an aggregate directional effect, both for genes and non-genic regions, such that the deletions in aggregate were associated with increased risk. There was also some suggestion that duplications, in aggregate, were associated with a reduced risk. These results suggest that additional associations are present that could be established with a larger dataset. A new GWAS, Con uence (https://dceg. cancer. gov/research/cancer-types/breast-cancer/con uence-project), aims to double the available sample size for breast cancer.
Acknowledgements We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. The COGS study would not have been possible without the contributions of the following  Andrew Berchuck  (OCAC),  Rosalind A. Eeles ,  Ali Amin Al Olama ,  Zso a Kote-Jarai ,  Sara Benlloch  (PRACTICAL),  Antonis Antoniou ,  Lesley McGuffog  and  Ken  O t (CIMBA),  Andrew Lee , and  Ed Dicks ,  Craig Luccarini  and the staff of the  Centre for Genetic Epidemiology Laboratory , the staff of the CNIO genotyping unit,  Jacques Simard  and  Daniel C. Tessier ,  Francois Bacot ,  Daniel Vincent ,  Sylvie LaBoissière  and  Frederic Robidoux  and the staff of the  McGill University and Génome Québec Innovation Centre ,  Sune F.
Genotyping of the OncoArray was funded by the  NIH  Grant  U19 CA148065 , and  Cancer UK  Grant  C1287/A16563  and the  PERSPECTIVE  project supported by the  Government of Canada through Genome Canada  and the  Canadian Institutes of Health Research  (grant  GPH-129344 ) and, the  Ministère de l'Économie, Science et Innovation du Québec through Genome Québec  and the  PSRSIIRI-701  grant, and the  Quebec Breast Cancer Foundation. Funding for iCOGS came from: the  European Community 's  Seventh Framework Programme  under grant agreement n°  223175  ( HEALTH-F2-2009-223175 ) (COGS),  Cancer Research UK  ( C1287/A10118 ,  C1287/A10710 ,  C12292/A11174 ,  C1281/A12014 ,  C5047/A8384 ,  C5047/A15007 ,  C5047/A10692 ,  C8197/A16565 ), the  National Institutes of Health  ( CA128978 ) and  Post-Cancer GWAS initiative  ( 1U19 CA148537 ,  1U19 CA148065  and  1U19 CA148112  -the  GAME-ON initiative ), the  Department of Defence  ( W81XWH-10-1-0341 ), the  Canadian Institutes of Health Research (CIHR)  for the  CIHR Team in Familial Risks of Breast Cancer , and  Komen Foundation  for the Cure, the  Breast Cancer Research Foundation , and the  Ovarian Cancer Research Fund.
The  DRIVE Consortium  was funded by  U19 CA148065. The  Australian Breast Cancer Family Study (ABCFS)  was supported by grant  UM1 CA164920  from the  National Cancer Institute (USA). The content of this manuscript does not necessarily re ect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the  USA Government  or the BCFR. The ABCFS was also supported by the  National Health and Medical Research Council of Australia , the  New South Wales Cancer Council , the  Victorian Health Promotion Foundation (Australia)  and the  Victorian Breast Cancer Research Consortium. J. L. H. is a  National Health and Medical Research Council  ( NHMRC) Senior Principal Research Fellow.
The BCEES was funded by the  National Health and Medical Research Council, Australia  and the  Cancer Council Western Australia  and acknowledges funding from the  National Breast Cancer Foundation (JS). For the BCFR-NY this work was supported by grant  UM1 CA164920  from the  National Cancer Institute. The content of this manuscript does not necessarily re ect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the  US Government  or the BCFR. The BCINIS study is supported in part by the  Breast Cancer Research Foundation (BCRF). For BIGGS, ES is supported by  NIHR Comprehensive Biomedical Research Centre, Guy's & St.
Thomas' NHS Foundation Trust  in partnership with King's  College London, United Kingdom. IT is supported by the  Oxford Biomedical Research Centre. The  BREast Oncology GAlician Network (BREOGAN)  is funded by  Acción Estratégica de Salud del Instituto de Salud Carlos III (ISCIII)   FIS PI12/02125/Co  nanciado  FEDER , and  ISCIII/PI17/00918/Co  nanciado  FEDER ;  Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud  ( PI13/01136 );  Programa Grupos Emergentes ,  Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS ,  Instituto de Salud Carlos III, Spain ; Grant  10CSA012E ,  Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain ; Grant  EC11-192.
Fomento de la Investigación Clínica Independiente ,  Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain ; and Grant  FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the  Dietmar-Hopp Foundation , the  Helmholtz Society  and the  German Cancer Research Center (DKFZ). CBCS is funded by the  Canadian Cancer Society  (grant #  313404 ) and the  Canadian Institutes of Health Research. CCGP is supported by funding from the  University of Crete. The CECILE study was supported by  Fondation de France ,  Institut National du Cancer (INCa) ,  Ligue Nationale contre le Cancer ,  Agence Nationale de Sécurité Sanitaire, de l'Alimentation, de l'Environnement et du Travail (ANSES) ,  Agence Nationale de la Recherche (ANR). The CGPS was supported by the  Chief Physician Johan Boserup and Lise Boserup Fund , the  Danish Medical Research Council , and  Herlev and Gentofte Hospital.
Financial support for the AOCS was provided by the  United States Army Medical Research and Materiel Command  [ DAMD17-01-1-0729 ],  Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia , The  Cancer Foundation of Western Australia, Cancer Council Tasmania  and the  National Health and Medical Research Council of Australia  (NHMRC; 400413, 400281, 199600). G. C. T. and P. W. are supported by the  NHMRC. RB was a  Cancer Institute NSW Clinical Research Fellow. LMBC is supported by the 'Stichting tegen Kanker'. DL is supported by the  FWO. The MABCS study is funded by the  Research Centre for Genetic Engineering and Biotechnology "Georgi D. Efremov", MASA. The MARIE study was supported by the  Deutsche Krebshilfe e. V. [ 70-2892-BR I, 106332 ,  108253 ,  108419 ,  110826 ,  110828 ], the  Hamburg Cancer Society , the  German Cancer Research Center (DKFZ)  and the  Federal Ministry of Education and Research (BMBF) Germany  [ 01KH0402 ].
The MASTOS study was supported by  "Cyprus Research Promotion Foundation " grants  0104/13  and  0104/17 , and the  Cyprus Institute of Neurology and Genetics. MBCSG is supported by grants from the  Italian Association for Cancer Research (AIRC). The MCBCS was supported by the  NIH  grants  R35CA253187 ,  R01CA192393 ,  R01CA116167 ,  R01CA176785  a  NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer  [ P50CA116201 ], and the  Breast Cancer Research Foundation. The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414  and  1074383  and by infrastructure provided by  Cancer Council Victoria.
Cases and their vital status were ascertained through the  Victorian Cancer Registry  and the  Australian Institute of Health and Welfare , including the  National Death Index  and the Australian Cancer Database. The MEC was supported by  NIH  grants  CA63464 ,  CA54281 ,  CA098758 ,  CA132839  and  CA164973. The MISS study is supported by funding from  ERC -2011-294576 Advanced grant,  Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation ,  Gunnar Nilsson. The MMHS study was supported by  NIH  grants  CA97396 ,  CA128931 ,  CA116201 ,  CA140286  and  CA177150. The work of MTLGEBCS was supported by the  Quebec Breast Cancer Foundation , the  Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program  -grant #  CRN-87521  and the  Ministry of Economic Development, Innovation and Export Trade  -grant #  PSR-SIIRI-701.
The content of this manuscript does not necessarily re ect the views or policies of the  National Cancer Institute  or any of the collaborating centers in the  Breast Cancer Family Registry (BCFR) , nor does mention of trade names, commercial products, or organizations imply endorsement by the  USA Government  or the BCFR. The  Carolina Breast Cancer Study (NCBCS)  was funded by  Komen Foundation , the  National Cancer Institute  ( P50 CA058223 ,  U54 CA156733 ,  U01 CA179715 ), and the  North Carolina University Cancer Research Fund. The NHS was supported by  NIH  grants  P01 CA87969 ,  UM1 CA186107 , and  U19 CA148065. The NHS2 was supported by  NIH  grants  UM1 CA176726  and  U19 CA148065. The OBCS was supported by research grants from the  Finnish Cancer Foundation , the  Academy of Finland  (grant number  250083 ,  122715  and  Center of Excellence  grant number  251314 ), the  Finnish Cancer Foundation , the  Sigrid Juselius Foundation , the  University of Oulu , the  University of Oulu Support Foundation  and the special Governmental EVO funds for Oulu University Hospital-based research activities.
The ORIGO study was supported by the  Dutch Cancer Society  ( RUL 1997-1505 ) and the  Biobanking and Biomolecular Resources Research Infrastructure  ( BBMRI-NL CP16 ). The PBCS was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the  Intramural Research Program  of the  National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the  Intramural Research Program of the Division of Cancer Epidemiology and Genetics  and supported by contracts from the  Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The RBCS was funded by the  Dutch Cancer Society  ( DDHK 2004-3124 ,  DDHK 2009-4318 ). The SASBAC study was supported by funding from the  Agency for Science, Technology and Research of Singapore (A*STAR) , the  US National Institute of Health (NIH)  and the  Susan G.
Komen Breast Cancer Foundation. The SBCS was supported by  She eld Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. SEARCH is funded by  Cancer Research UK  [ C490/A10124 ,  C490/A16561 ] and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. The  University of Cambridge  has received salary support for PDPP from the  NHS in the East of England through the Clinical Academic Reserve. The Sister Study (SISTER) is supported by the  Intramural Research Program  of the  NIH, National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES049033 ). The SMC is funded by the  Swedish Cancer Foundation  and the  Swedish Research Council  ( VR 2017-00644 ) grant for the  Swedish Infrastructure for Medical Population-based Life-course Environmental Research (SIMPLER).
The SZBCS was supported by Grant  PBZ_KBN_122/P05/2004  and the program of the  Minister of Science and Higher Education  under the name " Regional Initiative of Excellence " in 2019-2022 project number  002/RID/2018/19  amount of nancing  12 000 000 PLN. The TNBCC was supported by: a  Specialized Program of Research Excellence (SPORE) in Breast Cancer  ( CA116201 ), a grant from the  Breast Cancer Research Foundation , a generous gift from the  David F. and Margaret T. Grohne Family Foundation. The UCIBCS component of this research was supported by the  NIH  [ CA58860 ,  CA92044 ] and the  Lon V Smith Foundation  [LVS39420]. The UKBGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR), London. ICR  acknowledges  NHS  funding to the  NIHR Biomedical Research Centre.
The UKOPS study was funded by  The Eve Appeal (The Oak Foundation ) and supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre. The US3SS study was supported by Massachusetts (K. M. E. ,  R01CA47305 ),  Wisconsin  (P. A. N. ,  R01 CA47147 ) and  New Hampshire  (L. T. -E. , R01CA69664) centers, and  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The  USRT Study  was funded by  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Logan Walker  is supported by a  Rutherford Discovery Fellowship  ( Royal Society of New Zealand ).
Availability of Data and Materials Code for the CamCNV calling algorithm and a test dataset with OncoArray genotyping is available at the project home page (https://github. com/jgd29/CamCNV). Full summary statistics for the regions and probes analysed are available in the Supplementary Tables. The majority of the OncoArray dataset analyzed in this study is available in the dbGap repository, Study ID: phs001265. v1. p1 (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs001265. v1. p1). The iCOGS dataset and complete OncoArray dataset cannot be made publicly available due to restraints imposed by the ethics committees of individual studies; requests for data can be made to the corresponding author or the Data Access Coordination Committee (DACC) of BCAC (http://bcac.
a b s t r a c t Performing relevant mechanical tests requires complex experimental setups. CRAPPY is a Python module meant to help researchers develop code for systems involving several sensors and/or actuators. It features a number of advanced tools to perform measurements, drive hardware, and process data. CRAPPY aims at making the design of advanced tests easier and accessible to non-code experts. It is highly modular, and new devices can easily be added to the module as long as they can be interfaced with Python. Users can fully take advantage of Python's versatility in their experimental scripts, as they are not constrained by any GUI. CRAPPY features a wide variety of tools specific to experimental mechanics as it was first developed for this field, but tools and instruments from other domains can as well be included within its framework.
Each of these behaviors needs to be explored and studied. To this end, advanced setups are required. Such setups often imply driving multiple actuators according to pre-defined patterns, acquiring and saving data from various sensors, and plotting graphs for real-time monitoring. Properly synchronizing all these tasks proves challenging to non-code experts, and any change in the hardware used often entails re-writing large parts of the code. To address these limitations, CRAPPY provides a framework that internally manages the code complexity and aims at being accessible to beginners in Python. Tests are always driven by a single Python script, that is usually under 100 lines long. CRAPPY was also written to allow the devices to be easily interchangeable on the user's side. It comes with a readily available bank of devices and tools, including advanced techniques for experimental mechanics such as real-time Digital Image Correlation (DIC) or video-extensometry.