dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Novel protein family found in Y. lipolytica secretome -basic analysis of amino acid sequences Recently, high-throughput proteomics of Y. lipolytica W29 total secretome of strains producing heterologous proteins in industrial fermentation conditions (10 liters fermenter in feed batch mode) have been determined (Onésime et al, to be published). Secretome data mining with X!TandemPipeline allowed for identi cation of three proteins of unknown function, encoded by YALI0C05687g, YALI0D03245g and YALI0F04620g, with a coverage of 7. 08 (7. 66)%, 11. 66 (12. 62)%, and 19. 03 (20. 57)% (for the full length and the mature form), with an E-value of 50. 199, 62. 872 and 23. 919, respectively. Blast analysis against Y. lipolytica genome from GRYC database, showed that the proteins belong to a multi-gene family of four members (plus YALI0F04598g) of unknown function (Table  1  Comparison of the amino acid sequences showed that the four proteins are highly similar, as they share between 50 and 70 % of sequence identity (Fig.
1 ). In addition, all the proteins are equipped with a predicted 17 amino acid signal peptide, with a probability of 0. 9946 for D03245 (UP1) (MKFSHVTLAVVAATAIA), 0. 9991 for F04598g (UP2) (MQFSTLALVTFAATAMA), 0. 9926 for C05687g (UP3) (MKFSAVAVAAVASSALA), and of 0. 9995 for F04620g (UP4) (MKLSAVTFIALSAVCLA). For each protein, a similar 3D fold composed of six-helices bundle was predicted (Fig. 1  and section 3D structure modelling). Uniqueness of the UPs due to lack of similarity with other protein sequences Complete UPs sequences were subsequently blasted against available protein sequence databases. Strikingly, the only similarity was found with homologous sequences from the other Y. lipolytica strains (apart from E150 and W29, they are also present in the German H222 and Polish A101 strains; data not shown).
Since the screening of the protein database using the complete polypeptide sequences as queries was unsuccessful to sh out any signi cant hits beyond Y. lipolytica homologs, the conserved stretches of amino acid sequences found in the multiple alignment were used. Seven conserved motifs, numbered from 1 to 7, were identi ed within each of the UP sequence AAP [TS], APV[FY][TS]LAPxxFA, GFLDFSGY, GT[KR]FD[KQ]AVY[EA]F[IL][VI]NSGx[KS]DFL, [IF]LxSPLL, W[IL]FGxKQTVQ, [TS]GF[DN] RA, and served as queries to search against NCBI and UniProt protein sequence libraries. Expectedly, motif 1, localized to the signal peptide (at its C-terminus), was present in the highest number of protein sequences stored in NCBI and UniProt databases (3192524 and 2106782 hits, respectively). Similarly, motifs 5 and 7 were relatively frequently found (18712/13174 and 7511/5086 hits).
On the other hand, motifs 2, 4 and 6 were identi ed in only ve (NCBI database) or seven (UniProt) protein sequences within the respective library. Strikingly, irrespective of the queried database, the four UPs were among the identi ed motifbearing proteins. Such an outcome highlights that each of these conservative motifs is always accompanied by the other two, and that the motifs are speci c and unique for the newly identi ed protein family. Cloning and overexpression of the UPs genes. As no suggestion on biological function of the UPs could be inferred from similarity search of protein databases, overexpression strains were designed and constructed. Each UP gene was ampli ed with its speci c primer pair (additional Table  1 ) from W29 wild-type genomic DNA, and cloned as BamH1/AvrII fragment into JMP4230 (additional Fig.
3D structure modelling The four sequences of UP1 to UP4 share nearly 40% of amino acid residues identity and hence are clearly homologous. Therefore, they are expected to fold into a similar 3D structure. Similarity of primary structures of UP1 to 4, with respect to known proteins of the protein database, is too low to support consistent homology modelling. We therefore submitted the sequences of UP1-4 to AlphaFold2 computational tool. AlphaFold is a family of structure prediction tools based on deep learning which produces high quality predictions in a blind test of structure prediction (CASP14), also when no clear homologs are known  [38]. The 3D structures of the UP1-4, as modelled by AlphaFold, are highly similar to each other (Fig. 6 ) Their 3D structures are highly similar from position 70 (50 if numbered from the mature proteins) up to the position 221 at the end of the sequence (UP1 numbering) (Fig.
lipolytica strains; with no hit outside this group/species. Likewise, identi cation of conserved motifs and subsequent screening of databases, looking for akin pattern, which were expected to increase the probability to nd structural homologs were unsuccessful. Based on these analyses, we concluded that the newly identi ed four-member protein family is new and unique to Y. lipolytica species. To reveal biological function of proteins UP1-4, we generated a series of Y. lipolytica strains overexpressing individually the UP-encoding genes, also in a quadruple deletant (Q4) background. The genes were cloned under strong inducible promoter, to assure high level synthesis of the UPs and their unambiguous identi cation within the modi ed strains secretomes (Fig. 2  and Table  4 ). Due to constitutive basic level synthesis of the UPs, only overexpression strains constructed in Q4 background were considered relevant for the UPs functional studies.
As none from the proteins deposited in the databases exhibited similarity with the UPs, it was not possible to make any supported presumptions on their putative function. Knowing biology, physiology and genomic structure of Y. lipolytica, and i) its high predisposition to thrive in the presence of hydrophobic substrates (HS; lipids, triglicerides, fatty acids) and ii) the expansion of multi-gene families involved in HS utilization, we hypothesized that the UP proteins might be involved in HS utilization. We therefore tested the constructed strains (Fig. 3 ) for growth in different fatty acid methyl esters. The results of drop tests (Fig. 4 ) suggested that indeed, the UPs might be involved in the assimilation of HS. Speci cally, we observed that Q4 strain was unable to grow in a minimal medium with methyl esters of C10 to C14.
The following motifs were identi ed as conserved: AAP [TS], GT[KR]FD[KQ]AVY[EA]F[IL] [VI]NSGx[KS]DFL, GFLDFSGY, [IF]LxSPLL, [TS]GF[DN]RA, W[IL]FGxKQTVQ, APV[FY][TS]LAPxxFA; residues in brackets are allowed for substitution. The Uniprot database was screened with the emboss Fuzzpro tool to extensively search for the sequences that could contain the combination of those motifs, hence to identify putative homologues. In parallel, to get functional information, the sequence proteins were submitted and scanned against the Prosite collection of motifs using the expasy/prosite webserver (https://prosite. expasy. org/scanprosite/). Blast and 3D structure analysis. HHpred was used to search for structural homologous in the protein database  [52]. Since no structural homologues could be retrieved from the PDB at this stage, we went on using AlphaFold structure prediction, based on Arti cial Intelligence  [38].
Funding Jean-Marc Nicaud  received grant from the project  YaLiOl supported by the ANR  grant " ANR-20-CE43-0007 " of the  French National Research Agency (ANR) in France. Jean-Marc Nicaud  and  Lea Vidal  received grants from the project  Val2O  supported by the  UPSaclay  in the frame of the Investments for the future managed by the  Agence Nationale de la Recherche  under the " Investissements d'avenir" program  with the reference Poc in Labs 2020-1011. In part, this study was nancially supported by  Ewelina Celinska  grant.
Introduction Consortia such as the 100,000 Genomes Project  (Turnbull et al. , 2018) , GEUVADIS  (Lappalainen et al. , 2013) , MetaSub  (Mason et al. , 2016)  and Tara Ocean  (Karsenti et al. , 2011)  have generated large collections of genomic, transcriptomic, and metagenomic sequencing data, respectively. Rather than deep coverage of a single sample, such datasets contain a collection of sequencing experiments across many samples. For example, the Tara Ocean project generated metagenomic sequencing data across ecological niches all over the oceans, totalling at least 171 thousand billions of nucleotides. Such valuables resources are unfortunately difficult to comprehensively analyze, since their size makes bioinformatics analyses difficult. Traditional sequence analyses such as alignment to a reference database or de novo assembly are both difficult and limited in the results they yield.
For instance, metagenome assembly of individual samples (e. g. using MetaSPAdes  (Nurk et al. , 2017) ) is often not able to reconstruct low abundance genomes and tends to collapse variants between close strains. Co-assembly of multiple samples pools together coverage from multiple sites to alleviate this but results in further loss in strain specificity. Alternatively, aligning raw sequencing data to genome databases is hindered by the incompleteness of those databases. A recently proposed alternative is to build an index of the raw sequencing data and then later query sequences of interest, e. g. genes or shorter sequence fragments around variants such as SNPs or indels. Traditional indexing approaches, such as those used by BLAST  (Altschul et al. , 1990)  or DIAMOND  (Buchfink et al. , 2015) , do not scale to those large collections  (Marchet et al.
, 2021). Instead, customized indexing methods have been under development. A recent review surveyed 20 tools that were all published in the last couple years, aiming to index large collections of sequencing data  (Marchet et al. , 2021) , for example BIGSI  (Bradley et al. , 2019) , HowDe-SBT  (Harris and Medvedev, 2019) , and Mantis  (Pandey et al. , 2018). These indexes are typically able to answer whether an arbitrary fixed-length sequence (k-mer) belongs to any of the samples, and, if so, which ones. Though much progress has been made, indexing a collection such as Tara Ocean has remained out of practical reach. The vast majority of these large-scale k-mer indexing tools are based on common building blocks, three of them being: 1) k-mer counting, which summarizes sequencing data into a set of k-mers along with their abundances, 2), k-mer matrix construction, which aggregates lists of k-mer counts over a collection of samples (e.
g. as in  Marchet et al. (2020) ;  Muggli et al. (2019) ) in the form of a k-mer/sample matrix with abundances as values, and 3) Bloom filters construction, where the k-mer presence/absence information for each sample is converted into a Bloom 1 filter to save space and allow fast queries. Note that these building blocks are not specific to k-mer indexing tools, e. g. 1) and 3) are commonly used in short-read de novo assembly, and 2) also appears in transcriptomics analysis  (Audoux et al. , 2017). Importantly, these three steps are often categorized as "pre-processing" in k-mer indexing papers (e. g. (Pandey et al. , 2018; Harris and Medvedev, 2019) ) and discounted from the running time of these indexing tools. Yet, for a dataset like Tara Oceans, these steps dwarf the running time of the subsequent index construction by up to several orders of magnitude.
Related works KMC  (Kokot et al. , 2017)  and DSK  (Rizk et al. , 2013)  are two disk-based k-mer counting tools. Counting k-mers is an operation that identifies the set of k-mers present within one (or multiple) datasets and records the abundance of each k-mer. Intuitively, k-mers having a low abundance (i. e. seen few times) are more likely to be the result of one or multiple sequencing errors within reads, while k-mers above a certain abundance threshold are more likely to be correct. In its original publication, DSK directly stored k-mers in a hash table by carefully controlling for memory and disk usage using partitioning. Recent versions of KMC and DSK are based on variants of an algorithm introduced by MSPKmerCounter  (Li et al. , 2015)  subsequently made popular by KMC. In a nutshell, sequencing reads are split into partitions stored on disk, then each partition is loaded in memory and k-mers are counted within them.
One of its key advantages, besides efficiency, is that as a byproduct it constructs a k-mer dictionary: i. e. a data structure that can efficiently associate values to k-mers and supports efficient queries. Jellyfish was notably used to perform kmer counting in HowDe-SBT. However, its key-value store feature was not used. Several other k-mer counting tools exist, however a recent benchmark  (Manekar and Sathe, 2018)  determined that KMC 3 is one of the most efficient ones on a single node. Simka  (Benoit et al. , 2016)  is a multi-sample k-mer counting and k-mer matrices construction tool that was used for metagenomics. It is based on a variation of the original DSK algorithm, modified to run on a distributed cluster. HowDe-SBT  (Harris and Medvedev, 2019 ) is a k-mer indexing method for large sequencing data collections, that extends the original concept of Sequence Bloom Trees (SBT)  (Solomon and Kingsford, 2016).
kmtricks: A modular pipeline and library for construction k-mer matrices and Bloom filters on large datasets In this section we give an overview of our software kmtricks (for "kmer matrix tricks"). A more in-depth presentation and algorithmic details are provided Section 4. Essentially, kmtricks is a set of software components that together perform joint multi-sample k-mer counting and color matrix construction. This allows to efficiently construct the data structures (e. g. Bloom filters) needed for indexing terabase-scale collections of samples. The components of kmtricks, along with an example execution, are presented in Fig. 1. We highlight the following features which differentiates kmtricks from related works: • Joint k-mer counting allows to rescue large amounts of k-mers that would otherwise be discarded when processing samples independently.
Dataset and computing setup The Tara Ocean experiments (Section 3. 3) were performed on large and complex sea water metagenomic data composed of 241 samples (distinct locations) by the Tara Ocean project  (Karsenti et al. , 2011). This dataset is composed of approximately 6. 5 thousand billion nucleotides, consisting of around 266 billion distinct k-mers (k = 20), among which 174 billions k-mers occur twice or more as estimated by ntCard  (Mohamadi et al. , 2017). Executions were performed on a TGCC node with 4x16-cores Intel Xeon E7-8860 2. 20 GHz with 3 TB of memory, on a SDD with 4. 5 GB/s and 800 MB/s sequential read write, using 60 threads. Description of the data, tool versions and command lines are provided in a companion Github website (see reference  (Lemane and Peterlongo, 2021) ).
We evaluated the effect of the k-mer rescue procedure on the Tara Ocean experiment. Intermediate results and dedicated scripts are provided in the companion github web site  (Lemane and Peterlongo, 2021). For each sample i, we computed the solidity threshold t i as the smallest value ≥ 1 such that the number of k-mers occurring t i times is smaller than 10% of the total number of k-mers. (using ntCard  (Mohamadi et al. , 2017) ). In a sample i, any k-mer with abundance higher or equal to t i is conserved. Applying the rescue procedure, we then rescued any k-mer whose coverage was in a range [1, t i ] whenever it had a coverage > t j in at least one other sample j. Hence a k-mer is considered as erroneous in a sample i if its abundance in i is lower than t i and there exists no other sample j in which the k-mer is seen at abundance higher or equal to t j.
We validated this strategy as follows. For each sample we computed 1. err th : the theoretical expected number of erroneous k-mers, 2. errone: the number of k-mers occurring only once, and 3. err unrescued : the number of k-mers that are still considered as erroneous after our rescue procedure. We then look at the ratio err unrescued /err th and compare it to the ratio errone/err th. The closer a ratio is to one, the better. The Tara Oceans dataset was mainly generated by HiSeq 2000 technology (222 samples out of 241), 8 samples were generated by HiSeq2500, 4 samples by GAIIx. For each of these technologies, we computed the theoretical error rate err th. Given raw sequencing data from Acinetobacter baylyi generated by these three sequencing technologies 1  we counted the number of erroneous k-mers (k = 20), i.
Partitioning kmtricks performs parallel k-mer counting following the classical paradigm of partitioning k-mers based on their minimizers and then constructing super-k-mers, as in KMC 2  (Kokot et al. , 2017). However, the process is newly modularized so that intermediate tasks correspond to separate programs. Conceptually, the set of all possible minimizers is first partitioned in the following balanced way: all partitions should contain a roughly equal total number of k-mers. This is performed by the km_-minim_repart module, using the GATB library  (Drezen et al. , 2014)  that implements a previously-known algorithm from DSK  (Rizk et al. , 2013). In hash counting mode, in order to take advantage of the partitioning scheme in the context of Bloom filters construction, we use partitioned Bloom filters (pBFs).
Counting The second step, performed in km_reads_to_superk, consists in computing for each sample its super-k-mers and writing them to their corresponding partitions on disk (Fig 3. a ). From those super-k-mers, k-mers (or their hash values, depending on whether hash counting is performed) are de-duplicated and the abundance of each distinct k-mer (or hash value) is determined within each partition (km_superk_to_-kmer_counts module). In hash counting mode, kmtricks optimizes the output in the following way. If k-mer rescue (see 3. 4) is not performed, bit-vectors are output immediately instead of a list of counted hashes (Fig 3. 1. b). One bit vector is output per partition and per sample. In other words, these bit-vectors correspond to pBFs built from the hashes. Otherwise if k-mer rescue is performed (still in hash counting mode), bit-vectors cannot be immediately output as counts of the same hash value must be examined over all samples.
Outputs kmtricks can output different sort of k-mer matrices. In k-mer mode: count or presence/absence k-mer matrices. In hash mode: hashes presence/absence vectors (kmer-aggregative) or pBFs vectors (coloraggregative), both seen as bit matrices. Of note, pBFs vectors can be converted into sample-specific Bloom filters that are compatible with the SDSL library  (Gog et al. , 2014)  and HowDe-SBT (km_output_convert module). All these outputs are readable and writable using kmtricks C++ library. In the case of k-mer counts matrix, a text output format is also supported.
Discussion We propose a novel method for efficiently counting k-mers across multiple samples and for generating Bloom filters. In addition to being the fastest method for generating Bloom filters over terabyte-scale collections, our approach kmtricks proposes a novel mechanism to filter erroneous k-mers using their co-occurrence across samples, i. e. going beyond filtering on a per-sample basis. This approach leads to significantly improved recovery of k-mers in metagenomes. In our tests on relatively small collections (100-674 RNA-seq datasets, with on average hundreds of millions of distinct k-mers per sample) the performance of kmtricks is roughly equivalent to the state of the art KMC k-mer counter combined with the Bloom filters construction procedure of HowDe-SBT. However kmtricks stands out on larger collections having higher number of k-mers per sample, such as Tara Ocean (> 6 TB of sequences, with several billions of distinct k-mers per sample).
For those, Bloom filter construction becomes a bottleneck and highlights the superior efficiency of the streamlined kmtricks pipeline. At a high level, kmtricks is able to output matrices either in columnmajor order or in row-major order, where rows can either be k-mers or hash values. This flexibility allows kmtricks to provide inputs for both types of indexing data structures: k-mer-aggregative and color-aggregative (as defined in  Marchet et al. (2021)  and recalled in the Methods section). Rowmajor order makes the presence/absence of a k-mer directly accessible across all samples, and is contiguous in memory. In column-major order, each Bloom filter is independent and provides information about the existence of all k-mers in one sample. Contemporary of kmtricks, the MetaGraph software  (Karasikov et al.
Acknowledgements This work used HPC resources from the  Très Grand Centre de Calcul of CEA  (http://www-hpc. cea. fr/fr/complexe/tgcc. htm) and the  GenOuest bioinformatics core facility  (https: //www. genouest. org). The authors are grateful to  Bob Harris  for discussion on HowDeSBT, and  Eric Pelletier & Jean-Marc Aury  who provided links to Tara and Acinetobacter datasets, and precious information about these data.
Methodology We study the dynamics of the Didymos binary system with self-gravitating, rubble-pile models of Didymos and Dimorphos using the N-body granular physics codes PKDGRAV and GRAINS. In addition, the GUBAS full two-body problem (F2BP) code is used as an additional point of comparison with rigidbody results. For convenience, we provide the current best estimates for the physical and dynamical parameters at the time of this writing in Table  1. These parameters can also be found in Appendix A of  Rivkin et al. (2021) , but are updated here with the latest values as of this writing based on the Design Reference Asteroid v. 3. 2 (DRA; DART mission internal document). These values may not necessarily match exactly those used in the simulations presented here, although they are all close and within the estimated uncertainties of the system.
PKDGRAV PKDGRAV is a massively parallel N-body tree code that can represent each component of the Didymos system as an aggregate of many spherical particles  (Richardson et al. 2000; Stadel 2001 ). In this work, the k-d tree code is not used, so the gravitational forces on each particle are computed by summing directly over all particles at each time step to ensure the highest possible accuracy at the expense of computational speed (i. e. , the full N 2 ( ) N-body problem). The contact forces on interacting particles are handled using the soft-sphere discrete element method (SSDEM), which allows for particles to slightly overlap each other with a mediating spring force as a proxy for particle deformation  (Schwartz et al. 2012). With SSDEM, a user can set parameters such as the restoring spring constant and coefficients of rolling and twisting friction to achieve the desired material properties  (Zhang et al.
GRAINS GRAINS is a N-body code that accounts for both gravitational and granular physics interactions between a large number of nonspherical particles  (Ferrari et al. 2017). Gravity computations are done using either a direct N 2 algorithm, which accounts for the mutual gravity between all particles in the system, or by using a GPU-based octree implementation of the Barnes-Hut algorithm  (Ferrari et al. 2020 ). As done with PKDGRAV, we do not use the tree implementation in order to ensure the highest accuracy of mutual gravity computations in this work. Contact and collision interactions are handled using the Smooth Contact Method module of the open-source multiphysics code CHRONO (Tasora et al. 2016). GRAINS has been used recently to study the stability and internal structure of Didymos  (Ferrari & Tanga 2022; Hirabayashi et al.