dataesr/scientific-paragraphs-categorization huggingface dataset:
To do so, they simulated different types of noise, augmenting contig data with noised sequences. Then, they trained a model with the double objective to minimize the reconstruction error between noised version of a same contig while spotting the differences between different contigs. This approach was based on the principles of contrastive learning ( [91] ). This concept of contrastive learning allows the network to spot if a new sequence is a noisy variation of a precedent sequence or a totally new one. The network learned to handle noise by itself and pulled together in the latent space different versions of a same contig while pushing away other ones. The latent space clustering remained similar to that of VAMB. Compatible with other binners, CLMB was more refined and outperformed them (MaxBin2, VAMB and MetaBAT2) on the same CAMI2 datasets.
Following this idea, [106, 119] use different normalization methods combined with autoencoders to extract exploitable features. These mathematical transformations create new representations from the data that are easier to use by DL. Many feature selection methods have been used to reduce data sparsity. [124] uses Ridge Regression algorithm on Gene Family Abundance to create lower dimension data that can then be analyzed with a CNN. While most data preprocessing methods use normalization or distribution algorithms, [125] bypasses the DL training step by directly using statistical binning August 3, 2023 August 3, 2023 21/54 22/54 MegaD [121] is a simple Neural Network specifically trained for large datasets that achieves competitive results with other methods like PopPhy-CNN ( [122]) or MegaR ( [123]) while ruining relatively fast on large datasets.
key challenge in DL for metagenomics is that training NN models can be computationally intensive, particularly when dealing with intricate designs and large multidimensional datasets. But the use of DL in the case of sequence binning and classification has flourished because besides performance it actually saves time. Alignment-based methods are already very time-consuming and computationally expensive, and DL is a good solution not only to improve performance, but accelerate sequence analysis-based algorithms. The reason is that a DL model, once properly trained, is quite fast to use. While it performs well in generalization, training requires a lot of data and iterations, but once the weights are set, inference is straightforward. Following this idea, large pre-trained models like [130] aim at finding generalized embedding matrices that can then be used directly with new datasets.
The widespread adoption of partial order graphs in TGS tools raises an interesting question: Could alternative MSA methods also be effective for TGS data alignment? We address this gap through a two-fold contribution: Firstly, we introduce MSA Limit, an automated toolkit designed to benchmark various MSA tools on TGS datasets against a reference sequence. Built on Snakemake  (Köster and Rahmann, 2012)  and Conda  (Grüning et al. , 2018)  environments, MSA Limit offers a user-friendly, easily installable, and flexible framework. A detailed description of the pipeline can be found in Section 2. Secondly, we present an extensive set of datasets and benchmark a range of MSA tools. These datasets span bacterial, yeast, and human genomes and serve as a comparative baseline for a selection of widely-used MSA tools from various backgrounds: MUSCLE  (Edgar, 2004) , T-Coffee  (Notredame et al.
Simulated Datasets For precision over reference and error profiles, we used simulated datasets created via PB-SIM2  (Ono et al. , 2021) , utilizing the E. coli K-12 strain as the reference (GenBank accession GCA 000005845. 2). The datasets are named by their error types: DEL (deletions only), INS (insertions only), SUB (substitutions only), and MIX. The MIX datasets contain a proportion of 23% substitutions, 31% insertions, and 46% deletions following an ONT error model. For each of these four error types, DEL, INS, SUB and MIX, we generated eight datasets showcasing different error rates, 1, 2, 5, 10, 15, 20, 25, and 30%, giving a total number of 32 datasets. 7/24.
Real Datasets We handpicked real datasets based on three criteria: a reliable reference sequence, sequencing depth exceeding 100x, and diverse estimated sequencing error rates. The reference sequence's credibility is pivotal, as a perfect sequence is elusive. To minimize discrepancies, we chose genomes derived from the same individual. When such a genome was not available, we required that complementary reads from alternative sequencing technologies, (Illumina or HiFi) were available for the same individual and assembled those reads to produce a reference genome. For each dataset, we estimated the sequencing error rate by aligning the ONT reads on the reference genome using minimap2. Ths list is avaibale in Homo sapiens We collected ONT data from the T2T consortium  (Nurk et al. , 2022) , and used the T2T-CHM13v2.
General behaviour of all MSA tools In our initial experiments, we utilized the nine MSA tools described in Subsection 3. 1 and the four real datasets referenced in Subsection 3. 2, namely E. coli Hifi, E. coli Illumina, BMB yeast, and Human. We assessed a broad range of genomic region sizes: 100, 200, 500, 1000, 2000, 5000, and 10,000 bases. Additionally, we varied sequencing depths: 10x, 20x, 30x, 45x, 50x, 60x, 100x, 150x, 200x. For each dataset, we took 10 random regions. A total of 22,680 experiments were conducted. Comprehensive results can be found in the data repository gitlab. cristal. univ-lille. fr/crohmer/msa-limit, while a concise summary is provided in Table  2. Of the nine MSA tools we tested, seven (abPOA, KALIGN, KALIGN3, MAFFT, MUSCLE, POA, and SPOA) processed all datasets effectively.
9/24 MSA results, it was notably resource-intensive, being slower and consuming significant memory. We were not able to run it on larger datasets, such as those with sequencing depth 150x or 200x. Therefore, for large regions or deep sequencing, T-Coffee was not feasible. Given the minor accuracy improvement and significant computational cost, its utility is limited for reads datasets. Moving forward, our analysis omitted Clustal Omega and T-Coffee, focusing on the seven other tools, which are more efficient.
Evaluation of MSA Quality We provide a more detailed analysis into the remaining seven MSA tools' performance across 100 dataset windows, as opposed to the 10 windows used in previous experiments. Human (estimated error rate: 6%) BMB yeast (estimated error rate: 11%) E. coli Illumina (estimated error rate: 16%) E. coli Hifi (estimated error rate: 17%) Influence of Genomic Region Size. In Figure  5 , it was observed that genomic region size, spanning from 100 to 10,000 bases, had a negligible impact on consensus identity rate across all sequencing depths, deviating by less than 1%. As a result, the following experiments determined the region size at 500 bases, unless stated otherwise.
10/24 Human (estimated error rate:6%) BMB yeast (estimated error rate:11%) E. coli Illumina (estimated error rate:16%) E. coli HiFi (estimated error rate:17%) Figure  6. Effect of sequencing depth on the consensus sequence identity rate for Human, BMB yeast, E. coli Illumina, and E. coli HiFi datasets. Each dataset is evaluated over 100 distinct regions of size 500. The X-axis represents the sequencing depth, while the Y-axis indicates the identity percentage between the consensus and reference sequences. The figures display the mean consensus identity rate along with the standard deviation. Influence of the sequencing depth. Figure  6  illustrates the correlation between consensus identity rate and sequencing depth. The human dataset, characterized by its low error rate, showed only minor discrepancies between tools.
In contrast, all other datasets, whose sequencing error rate is above 10%, exhibited distinct variability in tool performance. Although there's a general trend indicating that greater depth improves results, this isn't always the case. The data suggests that after achieving a depth of approximately 50x, further enhancements in most tools become stagnant. Surprisingly, tools like the POA family and MUSCLE sometimes underperform at increased depths. On the other hand, KALIGN, KALIGN3, and MAFFT consistently show improvement. For example, for Hifi E. coli datasets that exceed 50x depth, most tools stabilize within a 97. 5% to 98. 5% identity range. However, POA stands out, dropping below 96% at these depths. It's worth noting that while POA and SPOA perform exceptionally well at lower depths, KALIGN and MAFFT achieve their best results at higher depths.
Such variations highlight that the choice of the optimal tool largely depends on the 11/24 specific depth context. The considerable standard deviation, approximately 2% in most instances, emphasizes the significant fluctuation in accuracy depending on region selection. The same observations hold for Illumina E. coli and BMB yeast. Influence of the sequencing error profile. Understanding the error rates impact is essential since it can vary a lot accross employed technologies and datasets. Figure  6  from the previous paragraph already indicated that lower error rates yield higher accuracy consensus sequences. We delve further into this question using simulated data. In Figure  7 , we demonstrate how consensus identity rate varies with sequencing error rate, specifically following a ONT error distribution.
All tools, when tested on simulated data, yielded highly accurate consensus sequences when the error rate was below 10%. Beyond this threshold, most tools' accuracies plunged, with the exception of POA and SPOA that showed resilience against escalating error rates. Our results validate the selection of POA for processing high error rates, reminiscent of early ONT sequencings. Figure  7. Effect of error rate on the consensus identity rate on a simulated E. coli dataset. The dataset consists of 100 distinct regions, each of size 500 bases with a depth of 45x. Reads are generated following the MIX model: 23% substitutions, 31% insertions, and 46% deletions. The graph illustrates the mean consensus identity rate and standard deviation relative to the imposed error rate. Differing TGS techniques exhibit distinct error patterns.
Addressing Diploid Genomes and Heterozygosity In previous sections, polyploid genomes were not addressed. Our datasets primarily consisted of haploid genomes, with the Human genome being an exception. Given the low heterozygosity rate in the Human genome (estimated between 0. 1% and 0. 5%), our analysis assumed that a single sequence could adequately represent the reference genome, overlooking allelic variations. When analyzing heterozygous organisms, relying solely on a single reference sequence can lead to an overestimation of differences between reads and the reference, especially due to heterozygous local variations. Some contemporary methods can generate "polyploid" reference sequences by distinguishing distinct haplotypes through phasing  (Garg et al. , 2016). When multiple haplotypes are available, one approach is to assign each read to a specific haplotype and execute MSA Limit on each haplotype separately.
However, this method is not foolproof. Low polymorphism regions can be challenging to differentiate, especially with highly noisy reads. Due to mapability challenges, many reads may be misassigned, skewing the analysis. Fortunately, a significant portion of haplotype variations, such as SNPs (Single Nucleotide Polymorphisms), can be easily encoded in a reference sequence using the IUPAC code. In this section, we explore diploid genomes to determine if MSA tools can effectively process reads from distinct alleles. For this purpose, we crafted an artificial diploid genome, ensuring precise knowledge of both haplotypes. Constructing a Heterozygous Yeast Genome. Inspired by the experiments in the nPhase paper  (Abou Saada et al. , 2021) , we combined datasets from homozygous diploid strains of Saccharomyces cerevisiae to simulate heterozygous yeast genomes.
To emulate this, we utilized the BMB strain, introduced another strain (CCN), and combined them to create a "heterozygous yeast". The reference sequences for the diploid genome were crafted by aligning contigs from both strains using minimap2. The alignments were refined with Exonerate, leading to a consensus sequence where IUPAC symbols indicate heterozygous polymorphisms between the two alleles. Experimental Design. We selected 100 windows of length 500 bases, resulting in a total of 290 SNPs between the two alleles. For each window, we ran the MSA tools, generating one consensus sequence per tool, similar to the approach in section 3. Given the ploidy degree of two, we set the identity threshold for consensus at 70%, accommodating the sequencing error rate of the datasets. In this context, IUPAC symbols in the consensus sequences are intended to represent polymorphisms between the two alleles.
DISCUSSION From our experiments, several insights emerge. Foremost, Clustal Omega and T-Coffee appear to be the least suitable among the tested tools. As expected, the performance of other tools, namely MAFFT, MUSCLE, KALIGN, KALIGN3, abPOA, SPOA, and POA, enhances as the sequencing error rate diminishes. These tools exhibit commendable performance with contemporary sequencing data, especially when error rates are around 5%, as observed in the recent Human dataset. A deeper analysis reveals that POA might be superseded by its variants, abPOA or SPOA. KALIGN3, in comparison to its predecessor KALIGN, seems less compelling. MAFFT emerges as a balanced choice, while MUSCLE's performance is offset by its computational demands. Interestingly, the influence of sequencing depth is not uniform across tools.
Tools from the POA lineage are recommended for datasets with lower depths (10x or 20x), whereas MAFFT, KALIGN, and KALIGN3 excel with datasets having depths greater than 50x. Table  4. We compare the metaconsensus with the best and worst sequences obtained across different tools and against T-Coffee at various depths. We present the frequency with which the metaconsensus exhibits superior (>), equal (=), or inferior (<) identity compared to the best-performing tool for a given experiment, accompanied by the average difference observed. Similarly, we report the performance of the metaconsensus in comparison with the worst-performing tool and T-Coffee.
15/24 In Section 2, we highlighted the inherent limitations of tools when addressing SNPs in diploid genomes. In such scenarios, no tool provides a comprehensive solution, emphasizing the need for specialized tools. A pressing question arises: do different tools make errors at unique positions, or are there universally challenging patterns? If each tool errs differently, a combined approach could potentially boost accuracy. To investigate this, we developed a "meta-MSA" using consensus sequences from various tools. We then compared the accuracy of this "metaconsensus" with that of individual tool-specific consensus sequences. As shown in Table  4 , the metaconsensus often outperforms individual tools in certain scenarios. However, it doesn't consistently emerge as the top choice. Notably, it excels in datasets with low coverage and high error rates, and consistently outperforms the least effective tool.
CONCLUSION We introduced a robust pipeline to assess the proficiency of MSA tools in generating accurate consensus sequences from TGS data. With its user-friendly design, facilitated by Conda and Snakemake, we envision three straightforward purposes for our tool: benchmarking novel methods, aiding users and developers in refining or selecting a method best suited to their needs, optimizing parameters for their chosen methods to fit their data properties. In addition to this pipeline, we generated a comprehensive benchmark dataset, which allowed us to unveiled some unexpected results. For instance, the popular SPOA doesn't always emerge as the best, especially at higher depths. The optimal tool can vary based on the error profile and sequencing depth. Our results also confirmed that existing methods struggle to effectively capture heterozygoty, with a mediocre precision.
Here, gene sets or modules referred to canonical pathways annotated by domain experts or derived by external databases. In ROMA, the co-variance across samples of the genes composing the module is interpreted as the result of the action of a hidden factor on the expression of the genes. This setting corresponds to the simplest uni-factor linear model of gene expression regulation  (Schreiber and Baumann, 2007). The rROMA package provides novel functionalities for the analysis, visualization and reporting of active/inactive modules. It allows to highlight two possible configurations of the expression of the genes of a module under the effect of a regulatory factor: we called shifted module the case where the genes of the module are collectively displaced to one side with respect to the center of the distribution (Fig.
Results We applied rROMA to investigate the activity of pathways in airway epithelial cells from cystic fibrosis (CF) patients and from healthy donors. More precisely, we compared the transcriptomes of primary cultures of airway epithelial cells from patients (N=6) with those of healthy controls (N=6), based on RNAseq data publicly available in the NCBI's GEO database, under the accession ID GSE 176121  (Rehman et al, 2021). Here, the Molecular Signature Database MsigDB hallmark gene set collection  (Liberzon et al, 2015)  was selected to ease interpretation. However, to provide a more complete view of the biological processes involved in a study, rRoma can be applied using multiple pathway databases. rRoma was run by specifying the pathway database to use and the expression matrix to analyze, as shown in the accompanying vignette.
Funding This work was supported by the  European Commission 's  Horizon 2020 Program ,  H2020-SC1-DTH-2018-1 , " iPC individualizedPaediatricCure " (ref. 826121) and by the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ). MC was supported by  Fondation pour la Recherche Médicale (FRM) , and MN was supported by  Vaincre La Mucoviscidose (VLM)  grant  number20190502488.
Introduction Intellectual disability (ID) and spastic paraplegia (SPG) are central nervous system disorders with marked clinical and genetic heterogeneity. In both groups, non-specific and syndromic forms have been described with numerous genes identified in the past few years  (Ellison et al. , 2013; Elsayed et al. , 2021). The association of SPG with ID or MR (mental retardation, the out of use designation of ID) is frequent with 106 and 127 entries in the OMIM (Online Mendelian Inheritance in Man) database, respectively. Conversely, the triple combination of ID, SPG and glaucoma appears only once (OMIM#278050) with the description of two affected families: in four patients of both sexes in two sibships of a large inbred Swedish pedigree  (Heijbel and Jagell, 1981) , and in three male Canadian siblings born to first-cousin parents  (Chenevix-Trench et al.
7) and aligned with the Human reference genome (hg19) using the ELANDv2 algorithm. Genetic variation annotation was performed with the IntegraGen in-house pipeline (IntegraGen). Filtering was performed using Eris software (IntegraGen) with an autosomal recessive hypothesis. Variants with minor allele frequency (MAF) >1% in either the 1000 Genomes Project, the EXAC, or the gnomAD databases were excluded. Genetic segregation of the candidate variant with the disease in Family A was confirmed by Sanger sequencing of GRID1 exon 3. For Family B, DNA sample of the proband was shipped to Otogenetics, USA (CLIA lab). ~50 Mb of genomic DNA were captured on HiSeq 2500. Fragments were read 100-125 bp, paired end. The sample was uploaded onto DNAnexus software and 71. 5 million reads were aligned to the reference human genome (Hg19) (Mean on target coverage, X118).
Molecular modeling of GluD1 R161H mutant structure The protein was generated using Rat GluD1 receptor in complex with 7-chloro-kynurenate and calcium ions (PDB codes: 6KSS and 6KSP) as structure templates  (Burada et al. , 2020). The system with proteins and ligands was prepared in the CHARMM-GUI web server  (Jo et al. , 2008)  in order to generate a membrane around the protein and solvate with water and ions. A heterogeneous membrane made of POPC was chosen, and a TIP3 water model with NaCl (0. 15 M) counter ions was chosen for the solvation. The system was typed with a CHARMM36m force field, and NAMD protocol was used. The system was equilibrated through six constrained simulations for a total of 500 ps by gradually diminishing the force constraints at each steps. The following constraints were applied (each value represents an equilibration step): protein backbone (5/2.
1-HA-GluD1 WT have been described previously  (Perroy et al. , 2008 , Benamer et al. , 2018). The R 161 H and T 752 M mutations were introduced in GluD1 through site-directed mutagenesis using the QuikChange II XL kit (Agilent Technologies). For generating pCMV-HA-GluD1 WT , the full length coding sequence of the Grid1 cDNA (Genbank accession number: BC167177) was PCR amplified from clone A230054J23 (Mus musculus adult male hypothalamus cDNA, RIKEN full-length enriched library, Refseq AK138279, Source BioScience) and inserted into the pCMV-HA-C plasmid (Clontech). A stop codon was then added at the end of the Grid1 coding sequence upstream of the plasmidic HA tag, before inserting a HA tag after the predicted signal peptide. All constructs were verified with DNA sequencing. Recombinant lentiviruses LV-PGK-GluD1 WT -ires-GFP, LV-PGK-GluD1 R161H -ires-GFP and LV-PGK-GluD1 T752M -ires-GFP were used for transduction of neurons in primary cortical cell culture.
We next performed a WES analysis on two affected siblings in Family A and one of their parents. Variants were filtered according to quality criteria, potential pathogenicity, and population frequency (Minor Allele Frequency <1%). This allowed the identification of a homozygous missense mutation of the GRID1 gene (NM_017551. 2: c. 482G>A, p. Arg161His; hg19, chr10:87966159 C>T) within the 10q22q23 candidate region (Figure  1A ). This variant segregated in an autosomal recessive manner in all affected members of the family (Table  2 ), with both parents and an unaffected sibling found to be heterozygous carriers, and is predicted as "Disease causing" by Mutation Taster (score 0. 9697). Referred in dbSNP (rs771100097), the p. Arg161His GRID1 variant is found at heterozygous state in 4 individuals in gnomAD database (v2.
1. 1, Minor Allele Frequency, MAF=1. 60e-5), but is not reported as homozygous. For Family B, single (proband-only) WES was pursued, and brought to the identification of a homozygous missense variant in GRID1 (NM_017551. 3: c. 2255C>T, p. Thr752Met; hg19, chr10:87379729G>A) (Figure  1A ). Using Sanger sequencing, this variant was confirmed to segregate with the disease in the family, with both affected siblings homozygous for the variant (Table  2 ), both parents and an unaffected sibling found to be heterozygous carriers, and three additional unaffected siblings wild type for the variant. The p. Thr752Met variant is only found at heterozygous state, in 11 individuals from gnomAD database (MAF=3. 89e-5). Finally, we sequenced a cohort of more than 200 patients affected with SPG, isolated or associated with ID, but this search failed to identify additional variants in GRID1.