dataesr/scientific-paragraphs-categorization huggingface dataset:
7% of imputation errors and Beagle made only 3. 1% of errors. Yet, it took Beagle 14. 6 h to complete while snp_fastImpute only took 42 min (20 times less). We also note that snp_fastImpute made less 0/2 switching errors, i. e. imputing with a homozygous referent where the true genotype is a homozygous variant, or the contrary (Supplementary notebook 'imputation'). We also show that the estimation of the number of imputation errors provided by function snp_fastImpute is accurate (Supplementary Fig. S8 ), which can be useful for postprocessing the imputation by removing SNPs with too many errors (Supplementary Fig. S9 ). For the celiac dataset in which there were already missing values, in order to further compare computation times, we report that snp_fastImpute took <10 h to complete for the whole genome whereas Beagle did not finish imputing chromosome 1 in 48 h.
Discussion We have developed two R packages, bigstatsr and bigsnpr, which enable multiple analyses of large-scale genotype datasets in R thanks to memory-mapping. Linkage disequilibrium pruning, PCA, association tests and computation of PRS are made available in these software. Implemented algorithms are both fast and memory-efficient, allowing the use of laptops or desktop computers to make genomewide analyses. Technically, bigstatsr and bigsnpr could handle any size of datasets. However, if the OS has to often swap between the file and the memory for accessing the data, this would slow down data analysis. For example, the PCA algorithm in bigstatsr is iterative so that the matrix has to be sequentially accessed over a hundred times. If the number of samples times the number of SNPs remaining after pruning is larger than the available memory, this slowdown would happen.
For instance, a 32 GB computer would be slow when computing PCs on more than 100K samples and 300K SNPs remaining after LD thinning. The two R packages use a matrix-like format, which makes it easy to develop new functions in order to experiment and develop new ideas. Integration in R makes it possible to take advantage of the vast and diverse R libraries. For example, we developed a fast and accurate imputation algorithm for genotyped SNPs using the widely-used machine learning algorithm XGBoost available in the R package xgboost. Other functions, not presented here, are also available and all the functions available within the package bigstatsr are not specific to SNP arrays, so that they could be used for other omic data or in other fields of research. We think that the two R packages and the corresponding data format could help researchers to develop new ideas and algorithms to analyze genome-wide data.
Context and motivation Bioinformatics software dealing with biological data (large volumes and variety of data structures) are routinely put together to design sophisticated workflows (or pipelines). Fortuitously, many workflows can be decomposed into embarrassingly parallel problems: a task can be applied in parallel on similar datasets (e. g. several DNA samples). Yet, the long-term utility of these pipelines is often tenuous, hampered of interwoven scripts and software libraries, numerous files or complex dependencies between tasks. Scripting languages may be sufficient to design workflows. However, they usually lack the ability to handle dependencies between rules, re-entrancies (starting from an intermediate step rather than from scratch), or distributed computing features to handle time-intensive and multi-parametric tasks.
To overcome these problems, various workflow managers have emerged as popular tools in the field of bioinformatics  (Leipzig, 2017). They usually possess all relevant features to design workflows effectively. Consequently, developers have the luxury of choosing a framework amongst many based on personal choice. Influencing factors may include the programming language or the presence of a graphical user interface (GUI). Workflow managers like Galaxy  (Goecks, 2010)  provides GUI drag and drop capability allowing non-specialists to implement de novo pipelines. On the other end of the spectrum, command line interfaces (CLI, hereafter) are still widely employed. Indeed, most developers would prefer a light-weight framework that could be more flexible or accelerate the development of new pipelines.
The graphical interface: Sequanix Snakemake is a text-based workflow that uses Python and a definition language to define rules and workflow properties. A Snakemake pipeline is defined within a file called Snakefile (examples in Supplementary Material). Although not strictly required, pipeline parameters may be externalized within a configuration file in YAML or JSON formats. Sequana project provides a set of Snakemake pipelines dedicated to NGS analysis (e. g. RNA-seq, variant calling,. ). Hereafter, we distinguish the Snakemake pipelines provided in Sequana from other Snakemake pipelines. We refer to the former as Sequana pipelines and to the latter as Generic pipelines. The first difference being that every Sequana pipeline is made of a Snakefile and a configuration file whereas Generic pipelines may not have a configuration file.
The second difference is that the Sequana configuration files are in YAML format only. A third difference is that the configuration file in Sequana must define specific fields which refer to the location or type of inputs files (e. g. FastQ files). Sequanix interface is designed in PyQt, which is a Python binding of the cross-platform GUI toolkit Qt (https://www. qt. io/). The user interface consists of a main dialog (see Fig. 1 ), a Snakemake dialog and a Preferences dialog (See Supplementary Material). The Snakemake dialog is used to configure the Snakemake framework behavior (e. g. number of CPUs to be used) while selection and execution of a pipeline is performed in the main dialog as explained below. At the top of the main dialog (Fig. 1A ) one can switch between the Sequana or Generic mode.
We designed Sequanix to build upon the Snakemake framework and add important functionalities. We felt that users should not manually edit configuration files. Therefore, configuration files are loaded in a dedicated widget and their sections and parameters are interpreted and shown in the interface (Fig. 1C ). Some parameters are associated with specific widgets. For instance a parameter name ending in _file or _browser becomes a file or directory browser instead of a simple editable line. We also propose to annotate configuration files (YAML format) with comments written as Python docstrings (see Supplementary Material). Such comments are then interpreted and appear as tooltips in the GUI. We also implemented the ability for users to import a YAML schema file, which is used to further validate the configuration file (e.
Conclusion Sequanix provides a GUI for Snakemake workflows. Its simple interface makes it easy for non-specialists to implement existing production-ready pipelines that have been created by Snakemake developers. Users can load a pipeline, edit the configuration file via various widgets or dropdown menus (reducing typographical errors), execute the pipeline locally or on a cluster, and track the progress of the analysis. Although Sequanix was primarily developed to expose Sequana pipelines, it should also benefit to the community of Snakemake developers willing to provide a graphical interface to their users. Currently, Sequanix is included in Sequana, that is available on Bioconda (Gru ¨ning, 2017) under the package named Fig. 1. Main Sequanix dialog. One can switch between Sequana and Generic modes (top panel).
Eisenhardt (1989)  considers that inductive case studies can provide comprehensive descriptions of a new or evolving industry, mentioning the work done by  Kidder (1982). We used a semi-structured questionnaire made as broad as possible to respond to the different situations encountered, i. e. Westerners starting a new business in the country or locals discovering the specificity of a new sector. The data collected from the actors involved in the case of failure helped us make our questioning more accurate, mainly for the list of potential factors (see Tab. 3). As is made possible with an abductive methodology, we had some overlap between data collection and data analysis, as well as back-and-forth motions to complete information, induce reactions to unexpected messages and test the importance of the various factors identified.
Transferring Technology and Ways of Working Looking back at the first years of the integration, our respondents mentioned that the major worry concerned the technology, i. e. whether it would be possible to reach the same level of quality in Russia as in the EU, considering the habits inherited from the Soviet Union (factor 1. 5), the backwardness of the country, as well as the difficulty to find the appropriate employees or to train them (factor 1. 3). In all the cases covered, employees from other sites brought a large support to help Russian employees acquire the necessary knowledge. For technical transfers, expatriates were usually sent from other sites for periods between four months and one year. After the departure of foreign experts, locals were still connected to data banks gathering information on possible technical mistakes or malfunctions.
Computational identification of miR169 family members In our previous study, we identified miR169 family members in banana using small RNA sequencing  (Song et al. , 2016). The mature miR169 sequences of other plant species were obtained from miRbase database (Release 21. 0, http://www. mirbase. org/). The BLAST parameters were the default routine settings of the database. The Latin name of miR169 members of plant species were got from the miRbase though the function of online database "Search by miRNA name or keyword. ".
Prediction of miR169 target genes and functional analysis The putative target genes for ma-miR169 were predicted using the plant miRNA target prediction online software psRNATarget (http://plantgrn. noble. org/psRNATarget/) with the default parameter settings. We selected the V2 Scoring Schema (2017 release), and applied Musa genomics (Musa acuminate V2, Banana Genome Hub, http://banana-genome-hub. southgreen. fr/home1). psRNATarget is a new web server designed to integrate and analyze reverse complementary matching between target transcripts and small RNAs. Another important function is the evaluation of target-site accessibility through calculation of the unpaired energy utilized to unfold the secondary structure around the miRNA target site in the mRNA. The following parameters were used: penalty for opening gap = 2, penalty for extending gap = 0.
Phylogenetic analysis of miR169 family members Molecular evolutionary analyses were conducted using the MEGA 7. 0 program. To evaluate the reliability of the different phylogenetic groups among miR169 family members, a phylogenetic tree was constructed using the maximum likelihood algorithm in the MEGA 7. 0 program. Editing was performed in FigTree v1. 4. 2 (http://tree. bio. ed. ac. uk/ software/figtree/). The sequences were aligned using ClustalW. Based on the neighborjoining method, the molecular evolutionary history was inferred using 1,000 bootstrap replicates to assess the robustness of the tree branches.
Identification and genomic distribution of ma-miR169 family members in banana Based on our previous miRNA transcriptomic data  (Song et al. , 2016) , a total of 18 ma-miR169 members were identified from the banana genome. The sequences of the ma-miR169 family were 18-22 nucleotides in length, and the sequence similarity reached 73. 67% (Fig. 1A ). Evolutionary analysis clustered the ma-miR169 family members into two groups (Fig. 1B ). To identify other various plant ma-miR169 members, a genome-wide search was carried out using the miRNAs database, miRbase (http://www. mirbase. org/cgi-bin/browse. pl). A total of 209 mature miR169 sequences were predicted in 28 plant species (Additional File 1). The numbers of miR169 family members differed greatly among the various species. Compared with the number of miR169 members in other plant species, including 11 in Arabidopsis thaliana, three in Oryza sativa, eight in Zea mays, and 17 in Glycine max, the number in banana, 18 was the highest (Fig.
Prediction of ma-miR169 targets Based on the 18 mature ma-miR169 sequences in banana, we used all the banana gene coding sequences to predict the ma-miR169 targets with the psRNATarget online software    (Li et al. , 2017). A total of 25 targets were obtained, and the number of targets differed greatly for the different miR169 members. Compared with other miR169 members, ma-miR169h, ma-miR169f, ma-miR169c, ma-miR169d, ma-miR169i, and ma-miR169k had more targets, with 3, 3, 2, 2, and 2 targets, respectively (Additional File 3). In addition, we found a complicated relationship between miRNAs and their targets. ma-miR169b, ma-miR169c, ma-miR169e, ma-miR169g, ma-miR169i, ma-miR169j, ma-miR169k, ma-miR169l, ma-miR169m, ma-miR169n, ma-miR169p, and ma-miR169q had the same target (GSMUA_Achr8T16690_001); ma-miR169c and ma-miR169k had the same target (GSMUA_Achr11T16520_001); and ma-miR169h and ma-miR169f shared three targets (GSMUA_Achr10T02910_001, GSMUA_Achr4T09610_001, and GSMUA_Achr8T20870_001).
Functional annotation of target genes Within the molecular function category, a large number of target genes were assigned to transcription factors, cellular processes, binding, metabolic processes, and organelles. To understand the biological function of these target genes, we got the metabolic pathway of those targets using the KEGG database (KEGG PATHWAY Database, 2017; http://www. kegg. jp/kegg/). Five target genes (GSMUA_Achr1T03730_001, GSMUA_Achr2T19800_001, GSMUA_Achr4T12800_001, GSMUA_Achr8T20870_001, and GSMUA_Achr11T16520_001) were marked in five metabolic pathways. These pathways included glycine, serine and threonine metabolism; pentose and glucuronate interconversion; glycerolipid metabolism; amino sugar and nucleotide sugar metabolism; and starch and sucrose metabolism (Additional Files 4 and 5).
DISCUSSION The miR169 family is one of the largest and most conserved miRNA families in plants  (Xu et al. , 2016). The miR169 family plays an important role in plant responses to abiotic and biotic stresses  (Zhang, 2015). The miR169 members information of plant species can be obtained in the miRbase database, including miRNA precursors, target genes, however, due to the lack of a miR169 reference sequence in the banana genome, the miR169 family of banana is not available in miRbase currently, and little information is known. In this study, 18 miR169 family members were identified in banana, a greater number than that in many plant species  (Samad et al. , 2017; Zhang & Wang, 2015). These miRNA sequences from the ma-miR169 family are 18-22 nucleotides in length and are comparable to the miR169 family members in other plants  (Noman et al.
Data Availability The following information was supplied regarding data availability: 1. miRBase: the microRNA database. http://www. mirbase. org/cgi-bin/mirna_summary. pl?fam=MIPF0000012 2. GSMUA_Achr8G16690_001: https://phytozome. jgi. doe. gov/pz/portal. html#!gene? search=1&detail=1&method=4987&searchText=transcriptid:32311987 GSMUA_Achr4G12800_001: https://phytozome. jgi. doe. gov/pz/portal. html#!gene? search=1&detail=1&method=4987&searchText=transcriptid:32297417 3. Designed primers for target genes in the Banana Genome Hub, http://banana-genome-hub. southgreen. fr/primer_designer, after got the RT reverse primer, RT forward primer, and PCR forward primer. After setting relevant parameters, all primers designed were verified experimentally, and then the ones with good specificity are available in Additional File 7.
The statistical methods we have developed to do so are implemented in a package, fluoSurv, so that they can be applied using the R software (R Core Team 2018). Let Yt be a measure of autofluorescence at time t for a given insect; we define Zt as Zt = (log 10 Yt -log 10 Yt-1) 2 The expected value of Zt is the variance of log 10 Yt. We therefore expect a decrease in average Zt when the insect dies. We fitted a generalized linear model (glm) where Zt is assumed to be Gamma distributed with average z b before death and za after. In a first step of the analysis, we estimated za as the mean Zt in the last 5 hours of the experiment for all insects that we know have died during the experiment. We estimated z b as the mean Zt over the first 5 hours of the experiment, a time at which we assumed that all insects were alive.
Using these two values, we could compute the log-likelihood of the Gamma glm for any time to death t d. We could thus obtain a first estimate td of time to death by maximizing this log-likelihood. In a second step, we re-estimated za as the average zt for t ≥ td and run again the previous analysis, obtaining therefore a new estimate td. This second estimate (shown as red vertical lines in both panels of figure  1 ) proved to be more reliable than the first, and was therefore used in all analyses. It should be noted that in most cases the method returns the maximum time value when the insect is alive, as it should be. Figure  2A  illustrates a possible utilization of our method on a complete data set. Here time of death has been computed for each of the 96 insects of a plate. Injecting 96 insects took us about three hours.
Model description ORCHIDEE-MICT is an updated version of the ORCHIDEE land surface model with an improved and evaluated representation of high-latitude processes. Phase changes of soil water (freeze/thaw), three-layered snowpack and its insulating effects on soil temperature in winter, permafrost physics and its impacts on plant water availability and soil carbon profiles are all represented in this model  (Guimberteau et al. , 2018). Based on ORCHIDEE-MICT, ORCHIDEE-PEAT is specifically developed to dynamically simulate northern peatland extent and peat accumulation. ORCHIDEE-PEAT version 1 was evaluated and calibrated against eddy-covariance measurements of CO2 and energy fluxes, water table depth, as well as soil temperature from 30 northern peatland sites  (Qiu et al. , 2018)   2014 ) is incorporated into the model to simulate dynamics of peatland area, calibrated with a new dataset of wetland areas excluding permanent lakes (Sect.
The cost-efficient TOPMODEL Concepts of TOPMODEL  (Beven and Kirkby, 1979)  have been proven to be effective at outlining wetland areas in current state-of-the-art LSMs  (Kleinen et al. , 2012; Ringeval et al. , 2012; Stocker et al. , 2014; Zhang et al. , 2016)   et al. , 2013). CW-WTD wetlands are static and aim at representing the climatological maximum extent of active wetlands and inundation. We therefore compare simulated monthly maximum wetland extent over 1980-2015 with CW-WTD to calibrate TOPMODEL parameters. Note that lakes from the HydroLAKES database have been excluded from the CW-WTD map because of their distinct hydrology and ecology compared with wetlands  (Tootchi et al. , 2018).
Simulation protocol We conduct both site-level and regional simulations with ORCHIDEE-PEAT v2. 0 at 1° × 1° spatial resolution. Regional simulations are performed for the Northern Hemisphere (>30° N), while site-level simulations are performed for 60 grid cells containing at least one peat core (Table  S1 , Fig. S3 ). Peat cores used in site-level simulations are from the Holocene Perspective on Peatland Biogeochemistry database (HPPB)  (Loisel et al. , 2014). Both site-level and regional simulations are forced by the 6-hourly meteorological forcing from the CRUNCEP v8 dataset, which is a combination of the CRU TS monthly climate dataset and NCEP reanalysis (https://vesg. ipsl. upmc. fr/thredds/catalog/store/p529viov/cruncep/V7_1901_2015/cata log. html). All simulations start with a two-step spin-up followed by a transient simulation after the pre-industrial period (Fig.
Evaluation datasets for site-level simulations All peatland sites used in this study are from the HPPB database  (Loisel et al. , 2014). All the peat cores measured peat ages and depths (60 sites, Table  S1 ), hence are used to evaluate simulated peat depth, with sites being grouped into different peatland types, climate zones and ages. For peat cores where peat ages, depths, fraction of C and bulk density were recorded (15 sites marked in red in Table  S1 ), we construct vertical C profiles with this measured information to compare with our simulated C profiles.
Soil organic carbon stocks Simulated peatlands SOC is evaluated against: 1. The WISE database; 2. The IMCG-GPD. All the above-mentioned datasets used to evaluate ORCHIDEE-PEAT v2. 0 at regional scale are described in the Supplement Text S7. Peat depth  Gorham et al. (2007 Gorham et al. ( , 2012) )  and  Kremenetski et al. (2003)  collected depth and age of 1685 and 130 peat cores, respectively, from literature data on peatlands in North America (NA) and in the West Siberian lowlands (WSL). These compilations make it possible for us to validate peat depths simulated by ORCHIDEE-PEAT v2. 0 at regional scales, in addition to the detailed site-runs in Sect. 3. 3. 1. Compared to the HPPB database, these datasets lack detailed peat properties (i. e. C content, peatland type…), but contain more samples and cover larger areas.
Peat depth Fig. 6  shows measured and simulated peat depth in NA and WSL. Some peat cores are sampled from the Canadian Arctic Archipelago, southwestern US and the northern tip of Quebec, where there is no peatland in peat inventories / the soil database. These sites support the notion that the formation and development of peatland are strongly dependent on local conditions, i. e. retreat of glaciers, topography, drainage, vegetation succession  (Carrara et al. , 1991; Madole, 1976). We do not expect the model to capture every single peatland because it is a large-scale LSM. Therefore, cores that are not captured by the model are removed from further analysis. As shown in Fig. 3 , within a grid cell, sampled peat cores can have very different depths and / or ages. We calculate the mean depth of cores in each of the grid cells and compare it against the simulated mean depth.
Simulated peatlands area in 2009 is evaluated against: 1. World Inventory of Soil Emission potentials (WISE) database (Batjes, 2016) ; 2. An improved global peatland map (PEATMAP) by reviewing a wide variety of global, regional and local scale peatland distribution information (Xu et al. , 2018) ; 3. International Mire Conservation Group Global Peatland Database (IMCG-GPD) (Joosten, 2010) ; 4. Peatland distribution map by Yu et al. (2010).
Observed (estimates from peatland inventories and soil database) and 1082 simulated northern peatland area, countries are sorted in descending order according to 1083 the estimate of IMCG-GPD. Geosci. Model Dev. Discuss. , https://doi. org/10. 5194/gmd-2018-256 Manuscript under review for journal Geosci. Model Dev. Discussion started: 29 November 2018 c Author(s) 2018. CC BY 4. 0 License.
Acknowledgements: This study was supported by the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. AH received financial support from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement No. 643052  ( C-CASCADES  project). We thank  Zicheng Yu  for providing the peatland distribution map.