dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials availability The study did not generate new materials. Data and code availability iZMA6517, EXTREAM, and MBA codes are publicly available in this GitHub directory: https://github. com/ssbio/iZMA6517. SI-k cat codes are available in this GitHub directory: https://github. com/ChowdhuryRatul/kcat_iZMA6517. RNAseq project is deposited in Gene Expression Omnibus (GSE235654) and publicly available. All steps of the experiment, from growth conditions to bioinformatic analyses, were detailed in CATdb: http://tools. ips2. u-psud. fr. fr/CATdb/; Project: NGS2021_19_Rhizophagus according to the MINSEQE. Nutrients for the plant growth can be accessed in Table  S13. All statistical analyses were run in R 4. 0. 567 using code that implement the algorithms described in the papers that introduced these statistical tests.
METHOD DETAILS K-means clustering analysis K-means clustering  60  algorithm was used to classify different genes into different clusters. Number of clusters was determined using the Elbow method. The whole K-mean clustering was implemented in Python, using numpy, pandas, and sklearn modules. Default setting of K-mean clustering, mentioned in the sklearn, was not changed in this study. Number of clusters were determined using the elbow method (Figure  S24 ). The elbow method is a graphical approach to determining the best number of clusters (K) value in the K-means clustering algorithm. The elbow graph depicts the within-cluster sum-of-square values on the y axis for various K values. The ideal K value is the point on the graph when it forms an elbow. To perform the K-mean clustering, we used 4 clusters, 10 centroid seeds, 300 maximum iteration, and 10 Genome-scale metabolic model reconstruction Determining the primary set of reactions for stalk and kernel tissue A previously published leaf genome-scale model  27  was used as a scaffolding to reconstruct the stalk and kernel tissues.
Creating stalk and kernel models The primary set of reactions J Primary , vascular tissue transporters Vt and environmental nutrients J Nutrients supplied to stalk and kernel were combined to form the draft model for each tissue. When modeling stalk and kernel models, the nutrients include both those from environment, but also include those metabolites that can be imported from the vascular tissue. Inactive reactions, determined by using the microarray data and GPR relationships, were placed into a set termed J Inactive , which is unique for each tissue. Finally, spontaneous reactions and reactions with a GPR relationship that only contain genes in the ''always lowly expressed'' set were placed into the set J NotMeasured , which is by definition equivalent for each tissue. In order to ensure that each tissue-specific biomass reaction was not blocked given the available nutrients to the tissue, (i.
Similarly, if R 1 is a forward reaction then the bounds on reaction will be À 30% R 1 % 30. Same will be true for other reactions, such as R 2 and R 3. According to the EXTREAM algorithm, the transcript will be distributed equally based on the number of reactions a gene participates. Furthermore, the reference condition. v j;omics , can be calculated as following: R 1 = 15 mmol/gDW/hr R 2 = 5 mmol/gDW/hr R 3 = 10 mmol/gDW/hr From EXTREAM, if R 1 is a forward reaction then the bounds on reaction will be 0 % R 1 % 15. Similarly, if R 1 is a forward reaction then the bounds on reaction will be À 15% R 1 % 15. Same will be true for other reactions, such as R 2 and R 3. Thus, EXTREAM algorithm provides a tighter bounds on reaction fluxes compared to the E-flux algorithm and can be useful for realistic phenotypical predictions.
MBA To determine the metabolic bottleneck in a GSM, we proposed the following algorithm. Max v biomass Subjectto: X m j = 1 S ij v j = 0;i ˛I ð14Þ a j % v j % b j ; j fj 0 g ˛J ð15Þ v j 0 ;min; % v j 0 % v j 0 ;max ; j 0 ˛Jð16Þ ; cj 0 ˛J Here a j is the lower bound reaction v j and b j is the upper bound of reaction v j. Both a j and b j were calculated from the transcriptomics data and GPR association. v j 0 ;min is the expanded lower bound of the reaction j 0 and v j 0 ;max is the expanded upper bound of the reaction j 0. In this case, we set v j 0 ;min = À 1000 mmol gDW:hr and v j 0 ;max = 1000 mmol gDW:hr. We solved the optimization problem by maximizing the biomass v biomass for the new expanded flux space of each reaction j 0 in an iterative manner and then recorded the biomass growth rate.
Raw data cleaning The raw data were first cleaned by the Genoscope as follows. Illumina filter. To remove the least reliable data from the analysis, the raw data were filtered to remove any clusters that have ''too much'' intensity corresponding to bases other than the called base. The purity of the signal from each cluster was examined over the first 25 cycles and calculated as Chastity = Highest_Intensity/(Highest_Intensity + Next_Highest_Intensity) for each cycle. The default filtering implemented at the base calling stage allows at most one cycle that is less than the Chastity threshold (0,6). Genoscope filter. We remove adapters and primers on the whole read and low-quality nucleotides from both ends (while quality value is lower than 20) and then continue next steps with the longest sequence without adapters and low quality bases.
À 4   relative tolerance. Continued REAGENT or RESOURCE SOURCE IDENTIFIER IDT for Illumina RNA UD Indexes Set A ILLUMINA 20040553 (96 indexes, 96 samples) AMPURE XP SPRI REAGENT 60 ML BECKMAN COULTER A63881 Agencourt RNA clean XP BECKMAN COULTER A63987 Ethanol absolute for BM 500 ML Sigma-Aldrich 51976-500ML-F NovaSeq 6000 S4 Reagent Kit v1. 5 (300 cycles) ILLUMINA 20028312 Phi X Control V3 ILLUMINA FC-110-3001 R R Studio V 4. 0. 567 CATdb Complete Arabidopsis Transcriptome V 1. 0 database RNA-seq Data Gene Expression Omnibus GSE235654.
the universal bacterial 16S ribosomal RNA (rRNA) marker gene  13  because of the relatively low cost of next generation sequencing and the variety of tools available for bioinformatic processing. However, it still is challenging to access and harmonize such data to compare across studies, especially when its corresponding metadata is missing or hard to decipher  14. Motivated by the investigation of fiber-induced shifts in microbiota and the potential for re-analyzing sequencing data, we screened more than 1,500 abstracts and obtained data from 11 fiber intervention studies performed in healthy human subjects, for a total of 2,368 samples from 488 subjects. The purpose of publishing this data descriptor is to provide a detailed description of these valuable datasets, allow others to re-use the data that was carefully curated, and to promote data accessibility.
Methods Data collection and harmonization. We conducted a keyword search of published literature through the PubMed search engine (keywords: dietary, fiber, and microbiome) under the Best Match algorithm recommended by PubMed on May 9 th , 2020. The search yielded 977 abstract hits from 2010 to 2020 (https://pubmed. ncbi. nlm. nih. gov/). We also searched through all the records available in the database of open-source microbial management site Qiita  15  (https://qiita. ucsd. edu) on April 7 th , 2020 and found 528 microbiome studies including human and animal studies. From both sources, each abstract was carefully read to select studies with fiber interventions in healthy humans that included 16S rRNA amplicon sequencing data from fecal microbial communities (n = 34). We excluded studies in animals and unhealthy humans (Fig.
1 ). Corresponding authors and first authors were contacted up to 4 times requesting their sequencing data and metadata when not publicly available. We were able to obtain 16S rRNA amplicon sequencing data and their corresponding metadata from 11 studies (Table  1 ). Data was shared to us via accession number  [16] [17] [18] [19] [20] [21] [22] [23]  or, if not publicly available, via virtual box. For the studies that did not make their datasets available at the time of publication (Dahl_2016_V1V2, Hooda_2012_V4V6, and Morales_2016_V3V4), we received consent to deposit their data under the BioProject ID: PRJNA891951 to the NCBI Sequence Read Archive 24. For these studies, we recommend downloading the raw data through the SRA Run Selector Tool that allows users to see the Library Name. Each Library Name includes the study name followed by an underscore and the Sample ID.
These Sample IDs are described in the metadata files created for this manuscript (see Data Records and Harmonization of datasets for more information). All studies included in this data repository complied with their relevant ethical regulations and have consent from their human participants to collect and share the data. For more information regarding guidelines for study procedure and trial registration numbers we refer our readers to the individual studies referenced in Table  1  and Table  2. The naming scheme for each of the studies included in this data collection is the following: Last name of the first author in the publication, followed by the year the study was published, and ending with the amplified region of the 16S rRNA bacterial gene (e. g. , Liu_2017_V4). We provide Table  2  with a summary of each of the studies which includes: number of interventions per study, fibers used and their amounts, length of interventions, number of colletion timepoints, subjects and total samples.
Because the metadata available was heterogeneous across studies, we performed harmonization across the datasets, so that common variables across studies could be easily identified. The metadata dictionary (Table  3 ) contains the definition for the data collected across studies. To provide as much information on the dietary fiber interventions as possible, we investigated the specific fibers that were used in each study. Table  4  shows all the dietary fibers that were used in the interventions and their manufacturer or recipe (when available) including controls. Sequencing processing. Individual studies used different methods for sequencing processing and bioinformatic pipelines, and such differences can influence the diversity and composition of microorganisms detected in a sample as well as the variation observed across samples  25.
Thus, to compare the sequences directly across studies, we obtained the raw sequencing reads for each study and then processed them in a similar manner. First, we assessed the quality of the 16S rRNA sequencing data using FastQC software 26 (version 0. 11. 8). The sequencing reads were cleaned from poor quality sequences using the Fastp program  27  (version 0. 20. 0). The cleaned sequences were imported into the QIIME2 platform  28  (version 2020. 11. 1), and primers were removed using Cutadapt 29 plugin when necessary. We then denoised the reads using DADA2  30  plugin, obtaining an OTU table depicting the number of reads per sample for each taxonomic unit (Fig. 2 ). Table  2. Summary of data collected by study. Shows the studies included in this data descriptor and their pertinent information such as fibers used, duration of intervention, number of subjects, etc.
Next, the taxonomic classification of the reads was also performed in the QIIME2 platform by training the SILVA  31  (version 132_99_16S) and the Genome Taxonomy Database 32 (GTDB; version bac120_ssu_reps_r95) databases to each respective study based on the primers that were originally used (Fig. 2 ). The SILVA database was used to remove chloroplast and mitochondrial DNA. Then, the cleaned reads were assigned to a final taxonomic group using the GTDB trained database. Reads that were not classified at least to the phylum level were removed from the analysis; sequences were classified to the finest level when possible (e. g. , species and/ or strain). The sequencing processing and taxonomic classification was performed with both the forward and reverse reads when paired-end data was available.
Data Records The following data have been deposited in the Figshare 33 repository: 1) The compressed 16S rRNA sequencing reads (. fastq. gz) containing the amplicon data that were quality filtered as described above; 2) the metadata files per study in tab-delimited format (. txt) describing their corresponding samples serving as a reference to help identify and sort the DNA sequences by different metrics (e. g. , timepoint, treatment, individual, etc. ); 3) the OTU tables with taxonomic assignment per study (. tsv) presenting the number of reads per sample for each taxonomic unit. As mentioned in the Data collection section and in Table  1 , the raw reads for the studies mentioned here can be found in publicly available databases  [16] [17] [18] [19] [20] [21] [22] [23]. For the studies that did not make their datasets available prior to this publication (Dahl_2016_V1V2, Hooda_2012_V4V6, and Morales_2016_V3V4), we received consent to deposit their data under the BioProject ID: PRJNA891951 to the NCBI Sequence Read Archive 24.
Technical Validation Data integrity. For quality assurance of the sequencing reads, we utilized the FastQC tool  26  as it provides quality control statistics such as sequence length, per base quality scores, and adapter contamination  34. We used the Fastp software  27  to ensure data integrity: we removed low quality reads from all datasets, only keeping reads with an average quality score of 30, the average score of 25 was chosen in only two occasions (Rasmussen_2017_ V1V3 and Liu_2017_V4) because read counts dropped dramatically with a higher threshold (-average_qual 30 or 25); we discarded sequences shorter than 100 bp (-length_required 100) to remove small sequences that could not complete 16S rRNA amplicon fragments. We only had to remove adapter contamination from one study (Deehan_2020_V5V6) using the detection of adapter correction tool in Fastp (-detect_adapter_for_pe).
Harmonization of datasets. To ensure the datasets were comparable, we converted sequencing reads from all studies into. fastq extension files (when necessary). Furthermore, we followed the same pipeline using consistent software and versions (Fig. 2 ) and cross-validated our results by visually inspecting the sequences after each clean-up step using Geneious prime (version 2020. 2. 4; https://www. geneious. com). For instance, after removing primers from reads using the Cutadapt plugin in QIIME2, we extracted the reads and imported them  into Geneious to verify that sequences had been properly trimmed. Moreover, to ensure clarity and consistency of metadata across datasets, we created a metadata dictionary (Table  3 ) to explain the data type (categorical, numerical, text, etc. ). In most cases, the metadata files available for the studies did not follow a consistent report of variables.
Study Name Repository for raw data Accession number for raw data Sequencing platform used Single-or paired- end data Processed data in this manuscript deposited to Figshare repository include Baxter_2019_V4 35 NCBI Sequence Read Archive SRP128128 Illumina MiSeq paired cleaned reads, metadata, OTU tables Dahl_2016_V1V2 36 NCBI Sequence Read Archive SRP403421 Illumina MiSeq paired cleaned reads, metadata, OTU tables Deehan_2020_V5V6 2 NCBI Sequence Read Archive SRP219296 Illumina MiSeq paired cleaned reads, metadata, OTU tables Healey_2018_V3V4 37 NCBI Sequence Read Archive SRP120250 Illumina MiSeq paired cleaned reads, metadata, OTU tables Hooda_2012_V4V6 38 NCBI Sequence Read Archive SRP403421 454/Roche pyrosequencing single cleaned reads, metadata, OTU tables Kovatcheva_2015_V1V2 39 NCBI Sequence Read Archive SRP062889 454/Roche pyrosequencing single cleaned reads, metadata, OTU tables Liu_2017_V4 40 European Nucleotide Archive PRJEB15149 Ion Torrent single cleaned reads, metadata, OTU tables Morales_2016_V3V4 41 NCBI Sequence Read Archive SRP403421 Illumina MiSeq paired cleaned reads, metadata, OTU tables Rasmussen_2017_V1V3 42 NCBI Sequence Read Archive SRP106361 454/Roche pyrosequencing single cleaned reads, metadata, OTU tables Tap_2015_V3V4 43 European Nucleotide Archive PRJEB2165 454/Roche pyrosequencing single cleaned reads, metadata, OTU tables Venkataraman_2016_V4 44 NCBI Sequence Read Archive SRP067761 Illumina MiSeq paired cleaned reads, metadata, OTU tables.
g. MMseqs2  (Steinegger and Söding, 2017) , allow for identifying homologous sequences and aligning them on a mass scale. Others relying on profile hidden Markov models (HMMs), such as JackHMMer/HMMer  (Eddy, 2011) , carefully generate very large families, achieving a very high sensitivity. Several large-scale resources like Pfam  (Mistry et al. , 2021)  and  ProteinNet (AlQuraishi, 2019)  give access to pre-computed multiple sequence alignments (MSAs) built from profile HMMs. These MSAs are associated with curated protein families in Pfam, or with experimentally resolved protein 3D structures in ProteinNet. The depth, quality, and computational cost of a MSA are important factors contributing to its effective usefulness. Nevertheless, precisely assessing the impact of expanding or filtering out sequences on predictive performance is difficult.
For protein structure prediction, Mirdita and co-authors showed that AlphaFold2 original performance could be attained with much smaller and cheaper alignments through the MMseqs2  (Steinegger and Söding, 2017) -based strategy implemented in ColabFold  (Mirdita et al. , 2022). This advance makes accurate protein structure prediction more accessible and applicable at a much larger scale. In this work, we aimed at testing whether the same gain could be obtained for mutational outcome prediction. We compared the prediction accuracy achieved by Global Epistatic Model for predicting Mutational Effects (GEMME)  (Laine et al. , 2019)  from MSAs generated using the ColabFold's MMseqs2-based protocol  (Mirdita et al. , 2022; Steinegger and Söding, 2017)  versus three workflows relying on profile HMMs  (AlQuraishi, 2019; Mistry et al.
For edge cases, where the filter is too drastic, we propose a simple solution to overcome the issue. We facilitated the import of alignments generated by ColabFold into the GEMME webserver, simplifying accessibility for users at: http://www. lcqb. upmc. fr/GEMME. Moreover, we provide predictions for the entire human proteome at: https://doi. org/10. 5061/dryad. vdncjsz1s. The other datasets generated and/or analysed during the current study are available in the same Dryad repository.
Results and Discussion We refer to the four different MSA generation protocols and resources we considered as ColabFold, ProteinGym-MSA, ProteinNet and Pfam (see Methods, Figure  1 , and Additional file 1: Table  S1 ). They all proved useful for several applications, and they represent a variety of choices in terms of sequence database, search algorithm and sequence context. In short, ProteinGym-MSA relies on the profile HMM-based method JackHMMer  (Eddy, 2011)  to search sequences against UniRef100  (Suzek et al. , 2015) , a non-redundant version of  UniProt (uni, 2023). The MSAs generated with this protocol have been widely used to assess mutational outcome predictors  (Notin et al. , 2022; Hopf et al. , 2017). ColabFold uses the many-againstmany sequence search algorithm MMseqs2 against the same database as ProteinGym, namely UniRef100.
The MMseqs2 search strategy differs markedly from JackHMMer in that it uses the 30% sequence identity clustered database UniRef30  (Mirdita et al. , 2022)  as a proxy to Uniref100 to select sequences. This strategy involves a series of expansion and filtering steps with different thresholds for which straightforward equivalents are not available in JackHMMer. Furthermore, ColabFold offers the possibility to include metagenomic data from the Big Fantastic Database (BFD)  (Jumper et al. , 2021). Both ProteinNet and Pfam are large readily available resources of MSAs generated from profile HMMs. Their advantage compared to the two other protocols is that they do not add any computational overhead on top of GEMME prediction itself. One potential drawback though is that they typically do not cover the full protein length and thus lack contextual information.
ColabFold alignments yield high-quality mutational landscapes with fewer sequences We found that ColabFold and ProteinGym-MSA were the best performing protocols and the only ones covering all ∼1. 5M mutations from the ProteinGym benchmark (Table  1 ). The MMseqs2-based ColabFold search strategy consistently yielded better predictions than the JackHMMer-based ProteinGym-MSA protocol for two thirds of the DMS (Figure  2A ). This result holds true whether the ColabFold protocol was performed against the union of UniRef100 and the ColabFold environmental database, which is the default set up, or only against UniRef100, i. e. the same database as used by ProteinGym-MSA (Additional file 1: Figure  S2 ). Moreover, the expand-and-filter strategy implemented in ColabFold produced shallower alignments, with substantially fewer sequences, than the other protocols (Additional file 1: Table  S1  and Figure  S3 ).
For instance, all proteins falling in the 'high' alignment depth category (N ef f /L > 100, see Methods) based on their ProteinGym-MSA alignments, would be reclassified in the 'medium' category (1 < N ef f /L < 100) based on their ColabFold MSAs (Figure  2B , red triangles, and Additional file 1: Figure  S4 ). This decreased alignment depth is accompanied by an improved prediction accuracy, by an average Spearman rank correlation difference ∆ρ = 0. 032, underlying the relevance of the ColabFold search strategy for these proteins. ColabFold also produced shallower alignments for most of the proteins from the 'medium' category (Figure  2B , blue triangles). The differences in alignment depths have a limited impact on the prediction accuracy except for two proteins, namely the polymerases PA and PB2 from the influenza A virus (Figure  2B , see the two outliers).
For these two extreme cases, the ColabFold MSAs are 20 times shallower than those produced by ProteinGym-MSA, resulting in a lower prediction accuracy by ∆ρ ∼ -0. 3. The reason behind such a difference is the low divergence of these protein families. Indeed, the ProteinGym-MSA alignments contain a few tens of thousands of sequences, but almost all of them are very similar to the query (Additional file 1: Figure  S5A-B , middle panels). GEMME is still able to exploit this limited variability to produce good-quality predictions (ρ values of 0. 586 and 0. 435). However, ColabFold's strategy massively filtered out these similar sequences, down to a few tens (Additional file 1: Figure  S5A-B , left panels). It brought in more divergent sequences, but they did not counterbalance the loss of information and GEMME predictions dramatically deteriorated.
Environmental sequences marginally contribute to improving predictions We assessed the contribution of the environmental sequences in the context of many-to-many sequence search with MMseqs2 and pHMMs-based search with JackHMMer (Figure  2C  and Additional file 1: Figure  S6 ). Augmenting Uniref100's set of annotated sequences with environmental sequences expands the ColabFold MSAs by up to 3 folds without significantly impacting the mutational landscape quality of most proteins (Additional file 1: Figure  S6A ). It slightly improved prediction accuracy for the four above-mentioned viral proteins, yet without allowing reaching a good agreement with the experimental measurements -the Spearman rank correlation remains below 0. 3 (Additional file 1: Figure  S6A , see purple dots at the bottom left).