dataesr/scientific-paragraphs-categorization huggingface dataset:
5c ). As noted by the authors of the original study by Franzosa et al. [28] , the cDNA conversion protocol used to produce the MT data is known to introduce approximately 1-2% of E. coli genomic DNA into the cDNA as contamination which is then reflected in the MT data. According to our analyses, 0. 12% of MG reads and 1. 95% of MT reads derived from this sample could be mapped onto the E. coli contigs, which is consistent with the numbers quoted by Franzosa et al. [28]. Consistent recovery of the E. coli genome was also observed across all other assemblies of the human fecal microbiome datasets (HF2-5) which included their respective MT data (Additional file 1: Figure  S4 ; Additional file 2: Table  S12 ). The integrative analyses of MG and MT data within IMP enables users to efficiently highlight notable cases such as this and to further investigate inconsistencies and/or interesting characteristics within these multi-omic datasets.
Discussion The microbiome analysis workflow of IMP is unique in that it allows the integrated analysis of MG and MT data. To the best of our knowledge, IMP represents the only pipeline that spans the preprocessing of NGS reads   S5 ) to the binning of the assembled contigs, in addition to being the first automated pipeline for reproducible reference-independent metagenomic and metatranscriptomic data analysis. Although existing pipelines such as MetAMOS or MOCAT may be applied to perform coassemblies of MG and MT data  [44] , these tools do not include specific steps for the two data types in their preand post-assembly procedures, which is important given the disparate nature of these datasets. The use of Docker promotes reproducibility and sharing, thereby allowing researchers to precisely replicate the IMP workflow with relative ease and with minimal impact on overall performance of the employed bioinformatic tools  [29, [46] [47] [48].
Furthermore, static websites will be created and associated with every new version of IMP (Docker image), such that users will be able to download and launch specific versions of the pipeline to reproduce the work of others. Thereby, IMP enables standardized comparative studies between datasets from different labs, studies, and environments. The open source nature of IMP encourages a community-driven effort to contribute to and further improve the pipeline. Snakemake allows the seamless integration of Python code and shell (bash) commands and the use of make scripting style, which are arguably some of the most widely used bioinformatic scripting languages. Snakemake also supports parallel processing and the ability to interoperate with various tools and/or web services  [49, 51]. Thus, users will be able to customize and enhance the features of the IMP according to their analysis requirements with minimal training/learning.
Quality control of NGS data prior to de novo assemblies has been shown to increase the quality of downstream assembly and analyses (predicted genes)  [63]. In addition to standard preprocessing procedures (i. e. , removal of low quality reads, trimming of adapter sequences and removal), IMP incorporates additional tailored and customizable filtering procedures which account for the different sample and/or omic data types. For instance, the removal of host-derived sequences in the context of human microbiomes is required for protecting the privacy of study subjects. The MT-specific in silico rRNA removal procedure yielded varying fractions of rRNA reads between the different MT datasets despite the previous depletion of rRNA (section "Tailored preprocessing and filtering of MG and MT data"), indicating that improvements in wet-lab protocols are necessary.
Furthermore, the analyses of the human fecal microbiome datasets (HF1-5) demonstrate that the numbers of MG reads mapping to the IMPbased iterative co-assemblies for each sample are comparable to the numbers of reads mapping to the comprehensive IGC reference database (Table  2 ). Previously, only fractions of 74-81% of metagenomic reads mapping to the IGC have been reported  [35]. However, such numbers have yet to be reported for MT data, in which case we observe lower mapping rates to the IGC reference database (35. 5-70. 5%) compared to IMP-based assemblies (Additional file 2: Table  S3 ). This may be attributed to the fact that the IGC reference database was generated from MG-based assemblies only, thus creating a bias  [35]. Moreover, an excess of 90% of MG and MT reads from the human fecal datasets (HF1-5) are mappable to either the IGC reference database and/or IMP-based iterative co-assemblies, emphasizing that a combined reference-based and IMP-based integratedomics approach vastly improves data usage (Table  2 ).
Although large fractions of MG and/or MT reads can be mapped to the IGC, a significant advantage of using a de novo reference-independent approach lies within the fact that reads can be linked to genes within their respective genomic context and microbial populations of origin. Exploiting the maximal amount of information is especially relevant for microbial communities with small sample sizes and which lack comprehensive references such as the IGC reference database. Fourth, the assembly refinement step via a contig-level assembly with cap3 improves the quality of the assemblies by reducing redundancy and increasing contiguity by collapsing and merging contigs (section "Assembly quality: multi-omic iterative co-assembly"). Consequently, our results support the described notion that the sequential use of multi-kmer-based de Bruijn graph assemblers, such as IDBA-UD and MEGAHIT, with overlap-layout-consensus assemblers, such as cap3, result in improved MG assemblies  [38, 62]  but importantly also extend this to MG and MT co-assemblies.
When compared to commonly used assembly strategies, the IMP-based iterative co-assemblies consisted of a larger output volume while maintaining a relatively high quality of the generated contigs. High-quality assemblies yield higher quality taxonomic information and gene annotations while longer contigs (≥1 kb) are a prerequisite for unsupervised population-level genome reconstruction  [14, 19, 56]  and subsequent multi-omics data integration  [39, 43, 44]. Throughout all the different comparative analyses which we performed, IMP performed more consistently across all the different datasets when compared to existing methods, thereby emphasizing the overall stability and broad range of applicability of the method (section "Assembly quality: multi-omic iterative co-assembly"). Integrated analyses of MG and MT data with IMP provide the opportunity for analyses that are not possible based on MG data alone, such as the detection of RNA viruses (section "Identification of RNA viruses") and the identification of transcriptionally active populations (section "Identification of populations with apparent high transcriptional activity").
The predicted/annotated genes may be used for further analyses and integration of additional omic datasets, most notably metaproteomic data  [39, 43, 44]. Furthermore, the higher number of complete genes improves the downstream functional analysis, because the read counts per gene will be much more accurate when having full length transcript sequences and will increase the probability to identify peptides. More specifically, the large number of predicted genes may enhance the usage of generated metaproteomic data, allowing more peptides, and thus proteins, to be identified.
Reproducibility IMP is implemented around a Docker container that runs the Ubuntu 14. 04 operating system, with all relevant dependencies. Five mounting points are defined for the Docker container with the -v option: i) input directory, ii) output directory, iii) database directory, iv) code directory, and v) configuration file directory. Environment variables are defined using the -e parameter, including: i) paired MG data, ii) paired MT data, and iii) configuration file. The latest IMP Docker image will be downloaded and installed automatically upon launching the command, but users may also launch specific versions based on tags or use modified/customized versions of their local code base (documentation at http://r3lab. uni. lu/web/imp/doc. html).
Automation and modularity Automation of the workflow is achieved using Snakemake 3. 4. 2 [49, 51] , a Python-based make language implemented specifically for building reproducible bioinformatic workflows and pipelines. Snakemake is inherently modular and thus allows various features to be implemented within IMP, including the options of i) executing specific/selected steps within the pipeline, ii) check-pointing, i. e. , resuming analysis from a point of possible interruption/termination, iii) analysis of singleomic datasets (MG or MT). For more details regarding the functionalities of IMP, please refer to the documentation of IMP (http://r3lab. uni. lu/web/imp/doc. html).
Paired-end FASTQ files are interleaved prior to running SortMeRNA. If one of the mates within the paired-end read is classified as an rRNA sequence, then the entire pair is filtered out. After running SortMeRNA, the interleaved paired-end output is split into two separate paired-end FASTQ files. The filtered sequences (without rRNA reads) are used for the downstream processes. All available databases provided within SortMeRNA are used for filtering and the maximum memory usage parameter is set to 4 GB (option: "-m 4000"), which can be adjusted in the IMP config file (section "Customization and further development").
The default BLAST and HMM databases of Prokka are used for the functional annotation. Custom databases may be provided by the user (refer to the "Databases" and "Customization and further development" sections for details). MetaQUAST 3. 1  [54]  is used to perform taxonomic annotation of contigs with the maximum number of downloadable reference genomes set to 20 ("--max-ref-number 20"). In addition, MetaQUAST provides various assembly statistics. The maximum number of downloadable reference genomes can be changed in the IMP config file (see "Customization and further development" for details).
Depth of coverage Contig-and gene-wise depth of coverage values are calculated (per base) using BEDtools 2. 17. 0  [71]  and aggregated (by average) using awk, adapted from the CONCOCT code  [16]  (script: map-bowtie2-markduplicates. sh; https:// github. com/BinPro/CONCOCT) and is non-configurable.
Visualization and reporting IMP compiles the multiple summaries and visualizations into a HTML report  [57]. FASTQC  [73]  is used to visualize the quality and quantity of reads before and after preprocessing. MetaQUAST  [54]  is used to report assembly quality and taxonomic associations of contigs. A custom script is used to generate KEGG-based  [74]  functional Krona plots by running KronaTools  [75]  (script: genes. to. kronaTable. py, GitHub URL: https://github. com/ EnvGen/metagenomics-workshop). Additionally, VizBin output (two-dimensional embeddings) is integrated with the information derived from the IMP analyses, using a custom R script for analysis and visualization of the augmented maps. The R workspace image is saved such that users are able to access it for further analyses. All the steps executed within an IMP run, including parameters and runtimes, are summarized in the form of a workflow diagram and a log-file.
Databases The IMP database folder (db) contains required databases required for IMP analysis. The folder contains the following subfolders and files with their specific content: i. adapters foldersequencing adapter sequences. Default version contains all sequences provided by Trimmomatic version 0. 32  [52]  ii. cm, genus, hmm, and kingdom folderscontains databases provided by Prokka 1. 11  [55]. Additional databases may be added into the corresponding folders as per the instructions in the Prokka documentation (https://github. com/tseemann/ prokka#databases) iii. sortmerna foldercontains all the databases provided in SortMeRNA 2. 0  [68]. Additional databases may be added into the corresponding folders as per the instructions in the SortMeRNA documentation (http://bioinfo. lifl. fr/RNA/sortmerna/ code/SortMeRNA-user-manual-v2.
Customization and further development Additional advanced parameters can be specified via the IMP command line, including specifying a custom configuration file ("-c" option) and/or specifying a custom database folders ("-d" option). Threads ("--threads") and memory allocation ("--memcore" and "--memtotal") can be adjusted via the command line and the configuration file. The IMP launcher script provides a flag ("--enter") to launch the Docker container interactively and the option to specify the path to the customized source code folder ("-s" option). These commands are provided for development and testing purposes (described on the IMP website and documentation: http://r3lab. uni. lu/web/imp/doc. html). Further customization is possible using a custom configuration file (JSON format). The customizable options within the JSON file are specified in individual subsections within the "Details of the IMP implementation and workflow" section.
Similar iterative assemblies were also performed for MT data using MEGAHIT  [23] , except CD-HIT-EST  [76]  was used to collapse the contigs at ≥95% identity ("-c 0. 95") while MetaGeneMark  [77]  was used to predict genes. The parameters and settings of the other programs were the same as those defined in the "Details of the IMP implementation and workflow" section. The aforementioned procedures were applied to all the datasets analyzed within this article. The merged contig sets (non-redundant) from the first iteration of both the MG and MT iterative assemblies were selected to represent the IMP single-omics assemblies (IMP_MG and IMP_MT) and were compared against co-assemblies.
Execution of pipelines MetAMOS v1. 5rc3 was executed using default settings. MG data were provided as input for single-omic assemblies (MetAMOS_MG) while MG and MT data were provided as input for multi-omic co-assemblies (MetA-MOS_MGMT). All computations using MetAMOS were set to use eight computing cores ("-p 8"). MOCAT v1. 3 (MOCAT. pl) was executed using default settings. Paired-end MG data were provided as input for single-omic assemblies (MOCAT_MG) while paired-end MG and MT data were provided as input for multi-omic co-assemblies (MOCAT_MGMT). All computations using MOCAT were set to use eight computing cores ("-cpus 8"). Paired-end reads were first preprocessed using the read_trim_filter step of MOCAT ("-rtf"). For the human fecal microbiome datasets (HF1-5), the preprocessed paired-and single-end reads were additionally screened for human genome-derived sequences ("-s hg19").
The resulting reads were afterwards assembled with default parameters ("-gp assembly -r hg19") using SOAPdenovo. IMP v1. 4 was executed for each dataset using different assemblers for the co-assembly step: i) default setting using IDBA-UD, and ii) MEGAHIT ("-a megahit"). Additionally, the analysis of human fecal microbiome datasets (HF1-5) included the preprocessing step of filtering human genome sequences, which was omitted for the wastewater sludge datasets (WW1-4) and the biogas (BG) reactor dataset. Illumina TruSeq2 adapter trimming was used for wastewater dataset preprocessing since the information was available. Computation was performed using eight computing cores ("--threads 8"), 32 GB memory per core ("--memcore 32") and total memory of 256 GB ("--memtotal 256 GB"). The customized parameters were specified in the IMP configuration file (exact configurations listed in the HTML reports  [57] ).
The analysis of the CAMI datasets were carried using the MEGAHIT assembler option ("-a megahit"), while the other options remained as default settings. In addition, IMP was also used on a small scale dataset to evaluate performance of increasing the number of threads from 1 to 32 and recording the runtime ("time" command). IMP was launched on the AWS cloud computing platform running the MEGAHIT as the assembler ("-a megahit") with 16 threads ("--threads 16") and 122 GB of memory ("--memtotal 122").
Data usage assessment Preprocessed paired-end and single-end MG and MT reads from IMP were mapped (section Read mapping) onto the IMP-based iterative co-assemblies and IMP_MG assembly. Similarly, preprocessed paired-end and singleend MG and MT reads from MOCAT were mapped onto the MOCAT co-assembly (MOCAT_MGMT) and the MOCAT single-omic MG assembly (MOCAT_MG). MetAMOS does not retain single-end reads; therefore, preprocessed MG and MT paired-end reads from MetA-MOS were mapped onto the MetAMOS co-assembly (MetAMOS_MGMT) and MetAMOS single-omic MG assembly (MetAMOS_MG). Preprocessed MG and MT reads from the human fecal datasets (HF1-5) were mapped using the same parameters described in the "Read mapping" section to the IGC reference database  [35]  for evaluation of a reference-based approach.
Assembly assessment and comparison Assemblies were assessed and compared using Meta-QUAST by providing contigs (FASTA format) from all different (single-and multi-omic) assemblies of the same dataset as input  [54]. The gene calling function ("-f") was utilized to obtain the number of genes which were predicted from the various assemblies. An additional parameter within MetaQUAST was used for ground truth assessment of the simulated mock (SM) community assemblies by providing the list of 73 FASTA format reference genomes ("-R"). The CPM measure was computed based on the information derived from the results of MetaQUAST  [54]. In order to be consistent with the reported values (i. e. , N50 length), the CPM measures reported within this article are based on alignments of 500 bp and above, unlike the 1-kb cutoff used in the original work  [62].
Analysis of contigs assembled from MT data A list of contigs with no MG depth of coverage together with additional information on these contigs (contig length, annotation, MT depth of coverage) was retrieved using the R workspace image, which is provided as part IMP output (sections "Visualization and reporting" and "Output"). The sequences of these contigs were extracted and subjected to a BLAST search on NCBI to determine their potential origin. Furthermore, contigs with length ≥1 kb, average depth of coverage ≥20 bases, and containing genes encoding known virus/bacteriophage functions were extracted.
Analysis of subsets of contigs Subsets of contigs within the HF1 dataset were identified by visual inspection of augmented VizBin maps generated by IMP. Specifically, detailed inspection of contiglevel MT to MG depth of coverage ratios was carried out using the R workspace provided as part of IMP output (sections "Visualization and reporting" and "Output"). The alignment information of contigs to isolate genomes provided by MetaQUAST  [54]  was used to highlight subsets of contigs aligning to genomes of the Escherichia coli P12B strain (E. coli) and Collinsella intestinalis DSM 13280 (C. intestinalis). An additional reference-based analysis of MetaQUAST  [54]  was carried out for all the human fecal microbiome assemblies (HF1-5) by providing the genomes of E. coli P12B and C. intestinalis DSM 13280 as reference (flag: "-R") to assess the recovery fraction of the aforementioned genomes within the different assemblies.
Acknowledgements We would like to acknowledge  John Larsson  from  SciLifeLab (Sweden)  for kindly providing the KEGG-based functional Krona plot scripts. Albi Celaj  from the  University of Toronto  is thanked for supplying the in silico simulated metatranscriptomic data and the corresponding reference genomes. The  University of Luxembourg High Performance Computing (HPC)  facility is duly thanked for providing and maintaining the computing platform. The Reproducible Research Results ( R3 ) team of the  Luxembourg Centre for Systems Biomedicine  is acknowledged for support of the project and for promoting reproducible research. Finally, we acknowledge the  Luxembourg National Research Fund (FNR)  for funding the project via the ATTRACT, AFR,  CORE ,  INTER  and PoC grants.
Funding This work was supported by an  ATTRACT program  grant ( A09/03 ), a  European Union Joint Programming in Neurodegenerative Diseases grant  ( INTER/JPND/12/01 ), a proof-of-concept grant ( PoC/13/02 ), an  ERASysAPP grant  ( INTER/8888488 ), and  CORE  programme grant ( CORE/15/BM/10404093 ) to PW, an  Aide à la Formation Recherche (AFR)  grant to SN ( PHD-2014-1/ 7934898 ), and a  CORE junior  ( C15/SR/10404839 ) to EELM, all funded by the  Luxembourg National Research Fund (FNR).
Coupled metagenomic and metatranscriptomic datasets The published human fecal microbiome datasets (MG and MT) were obtained from NCBI Bioproject PRJNA188481 (https://www. ncbi. nlm. nih. gov/ bioproject/PRJNA188481). They include samples from individuals X310763260, X311245214, X316192082, X316701492, and X317690558  [28] , designated within this article as HF1-5, respectively. Only samples labeled as "Whole" (samples preserved by flash-freezing) were selected for analysis  [28]. The published wastewater sludge microbial community datasets (MG and MT) were obtained from NCBI Bioproject with the accession code PRJNA230567 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA230567). These include samples A02, D32, D36, and D49, designated within this article as WW1-4, respectively  [43]. The published biogas reactor microbial community data set (MG and MT) was obtained from the European Nucleotide Archive (ENA) project PRJEB8813 (http://www.
Simulated coupled metagenomic and metatranscriptomic dataset The simulated MT data were obtained upon request from the original authors  [12]. A complementary metagenome was simulated using the same set of 73 bacterial genomes used for the aforementioned simulated MT  [12]. Simulated reads were obtained using the NeSSM MG simulator (default settings)  [79]. The simulated mock community is designated as SM within this article  [79]. The simulated data along with the corresponding reference genomes used to generate the MG data are made available via LCSB WebDav (https://webdav-r3lab. uni. lu/public/R3lab/IMP/datasets/) and is archived on Zenodo  [80].
CAMI simulated community metagenomic datasets The medium complexity CAMI simulated MG data and the corresponding gold standard assembly were obtained from the CAMI website (http:// www. cami-challenge. org). Test dataset for runtime assessment A subset of ~5% of reads from both the WW1 MG and MT datasets (section "Coupled metagenomic and metatranscriptomic datasets") was selected and used as the data to perform runtime assessments. This dataset could be used to test IMP on regular platforms such as laptops and desktops. It is made available via the LCSB R3 WebDav (https://webdav-r3lab. uni. lu/public/ R3lab/IMP/datasets/) and is archived on Zenodo  [81]. Software and source code IMP is available under the MIT license on the LCSB R3 website (http:// r3lab. uni. lu/web/imp/), which contains necessary information related to IMP.
These include links to the Docker images on the LCSB R3 WebDav (https:// webdav-r3lab. uni. lu/public/R3lab/IMP/dist/) and is archived on Zenodo  [82]. Source code is available on LCSB R3 GitLab (https://git-r3lab. uni. lu/IMP/IMP), GitHub (https://github. com/shaman-narayanasamy/IMP), and is archived on Zenodo  [83]. Scripts and commands for additional analyses performed specifically within this manuscript are available on LCSB R3 GitLab (https://git-r3lab. uni. lu/IMP/IMP_manuscript_analysis) and on GitHub (https://github. com/shaman-narayanasamy/IMP_manuscript_analysis). Frozen pages containing all necessary material related to this article are available at http://r3lab. uni. lu/frozen/imp/.