dataesr/scientific-paragraphs-categorization huggingface dataset:
Novel variants In the current study, we found two novel variants (7. 4%) c. 765G>A in exon 6, resulting in a synonymous variant (p. Gln255=) and c. 1565+3_1565+4delinsGT at intron 10. These variants have not been previously reported in the literature or in a variant-tracking database. In total, three potentially pathogenic mutations predicted by in silico tools were found in our study. These mutations were carried by three different patients who meet the 2015 HDGC test criteria (3/22 representing 13. 64 %) (Fig. 2 ). The rst case « JI-014 » had the c. 1565+3_1565+4delinsGT in intron 10. She was referred for a molecular screening of CDH1 as she was suspected of having HDGC from the Oncology Department of Military hospital in Tunisia. She was a 42 years old woman diagnosed with DGC (T4N1M1) at Antro pyloric and treated with Palliative Chemotherapy.
Screening of large deletions/duplications in the CDH1 gene unsig multiplex ligation-dependent probe ampli cation (MLPA) assay Since heterozygous large deletions or duplications may remain undetected by conventional sequencing, we searched for possible rearrangements of the CDH1 locus using the MLPA assay  [37]. Results were analyzed using the Coffalyser software (MRC-Holland, Amsterdam, Holland): for normal sequences a probe dosage ratio of 1. 0 is expected; probes with a dosage ratio <0. 7 or >1. 3 indicate deletions or duplications in the corresponding exons respectively. By comparing the control probes with the studied cases, we found that only the index case JI-020 had abnormal multiplex ligation-dependent probe ampli cation (MLPA) features with more than 45% reduction in signal in one or more exons of the CDH1 gene, indicating the presence of large deletions in the CDH1 locus.
CTNNA1 Genetic Testing A total of 34 Tunisian patients with DGC were selected to screen for CTNNA1 germline mutations. All identi ed variants are summarized in Additional le 7. The distribution of identi ed CTNNA1 variants is summarized in Fig. 6. All identi ed coding variants were synonymous, representing 8 out of 15 (53. 33%). Based on the ClinVar database, the variants were classi ed as benign (53. 33%) and a novel variant identi ed in two patients (JI-001 and JI-006) in intron 16 (c. 2193-68C>T) was predicted to be a polymorphism.
Discussion In the current study, we screened 34 DGC patients from unrelated families of North-East Tunisian region with suspected HDGC to shed light on the molecular basis of GC. This region is known to have a relatively high proportion of digestive cancer syndromes and diffuse gastric tumors. However, no epidemiological data are available. To do so, we performed a screening of the full coding and anking regions of both CDH1 and CTNNA1 genes as well as CDH1 large rearrangements. An IHC was used to investigate the Ecadherin protein expression pro le in the available GC FFPE tissues as well. To our knowledge, this is the rst study investigating the genetic mutation pro le of patients with HDGC syndrome in Tunisia and in North Africa. As results, we identi ed three probably pathogenic mutations as predicted by in silico prediction tools (a splice alteration, a large rearrangement and a missense probably pathogenic).
In addition to CDH1 mutations ,pathogenic variants in CTNNA1 are known to occur in a small proportion of families with HDGC  [23, 57]. Nevertheless, little is known about the penetrance of the CTNNA1 gene  [24]. In the current study, no pathogenic CTNNA1 variants were found. All coding variants were synonymous. Based on the ClinVar database, variants were classi ed as benign (53. 33%). A novel variant was identi ed in intron 16 (c. 2193-68C>T) in two patients (JI-001 and JI-006) which was predicted to be benign by all in silico prediction tools. Our results indicate that the genetic mutation pro le of studied patients with suspected HDGC is different from other families in other populations, as we did not nd any reported pathogenic CDH1 and CTNNA1 mutations. These ndings could be explained by the signi cant variability in GC frequency worldwide as well as risk factors  [1].
List Of Abbreviations HDGC Hereditary Diffuse Gastric AntroPyloric Figures Figure 1 Percentage of distribution of 27 CDH1 identi ed variants: Figure 2 Family History of index cases carrying selected variants. Figure 3 Indel effect for the index case "JI-014"as predicted by Alamut Interactive Biosoftware Figure 4 Detection of CDH1 exon deletions by MLPA assay. Figure 5 In silico analysis of the mutation c. 2281G>A on E-Cadherin. Figure 6 Distribution of 15 identi ed CTNNA1 variants.
A chronology that encompasses the various sequencing revolutions is highlighted in Figure  1. Such sequencers and their evolution drastically accelerated the numbers of completely sequenced bacterial genomes  [14]. For instance, thousand genome sequencing was achieved in 2007, the thousandth genome of Escherichia coli was achieved in 2014 and the 100th thousandth genome in 2017. Currently, more than 376,000 bacterial genomes projects are deposited and available in public databases (https://www. ncbi. nlm. nih. gov/genome/ browse/#!/prokaryotes/, accessed on 25 January 2021).
This global approach is being developed at the same time as the WGS is being facilitated. An example is the tracking of diffusion and monitoring the evolution of M. tuberculosis Beijing lineage  [41] , a very virulent and potentially antibiotic-resistant strain. Using a large dataset of a single M. tuberculosis lineage, Merker et al. identified the biogeographic structure and evolutionary history of the Beijing lineage worldwide through the SNPs analysis of 4987 isolates from 99 countries  [45]. They showed that this lineage originated in the Far East, from where it spread throughout the world in several waves. In addition, global SNPs genotyping was applied to Mycobacterium abscessus, a human skin bacterium. Choo.
Citing the example of the colistin mcr-1 resistance emergence surveillance survey conducted by Falgenhauer and colleagues in 2016  [52] , the authors built a database of 577 Enterobacteriaceae genomes obtained from different sources (human, animal and environmental), which was queried to identify four previously undiagnosed colistin-resistant isolates. In addition, they demonstrated the existence of multiple horizontal pathways of this resistance. In 2017, Jeukens et al. analyzed 59 sequence genomes of Achromobacter genera and identified genes involved in effluxmediated antibiotic resistance compared with the Comprehensive Antibiotic Resistance Database (CARD)  [53]. The resistome analysis showed that the clinical specimens carried more antibiotic resistance genes than other isolates  [54].
Furthermore, even if genomes are released as completed on public databases, the comparison of synteny rearrangements between closed species or comparisons of redundant regions are still problematic. Indeed, structural variations (SV) within the genomes play an important role and have to be assembled correctly. SV refers to chromosomal rearrangements typically classified as insertions, inversions, duplications, deletions and translocations describing resulting combinations of DNA losses or gains. Short-read sequencing is widely used for the identification of single nucleotide variants (SNVs) and small indels  [38, 71]. However, it could fail to detect larger structural variations properly, especially when several copies of these fragments exist in a genome  [38, 71]. In addition, the bad positioning of large genomic rearrangements can lead to misinterpreting the structural variants that may occur between the genomes of closed strains  [71] [72] [73] [74].
This system provides a web interface to many common bioinformatics programs, allowing users to perform files manipulation and analysis without the need to go through command lines. Other systems such as Omnics Pipe  [139]  are more for users who need to analyze a large dataset and automate data analysis pipelines for multiple NGS technologies. It includes a set of bioinformatics tools that can be combined in predefined pipelines. HTS-FLOW is another popular management system developed by the Instituto Italiano di Tecnologia in Milano  [140]. HTS-FLOW manages NGS analysis in a traceable way through a graphical interface, produces data in standard locations associated with metadata and script analyses. A recent tool, the One Touch Pipeline (OTP)  [141] , is a platform for structured data storage and NGS data management developed by the German Cancer Research Center (DKFZ).
It is designed to graphically manage routine NGS analyses in a scalable and automated manner, from importing raw sequence data to notifying project members of the completion of their analyses to aligning and identifying genomic events. Standardization of analyses is probably the main challenge of omics studies. New technologies and new bioinformatics tools appear regularly and in an unprecedented way, making it difficult to converge into similar analysis methods. The choice of bioinformatics methods and algorithms to be implemented implies finding a balance between computational speed, sensitivity and time allowed for the analysis. Most bioinformatics tools are opensource and freely available through platforms such as GitHub, GitLab or SourceForge. Some authors have tried to establish catalogs of tools commonly used in a particular field, such as for the use of long-read sequencing data (https://github.
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (i) tackle new tasks, like protein-ligand complex structure prediction, (ii) investigate the process by which the model learns, which remains poorly understood, and (iii) assess the model's generalization capacity to unseen regions of fold space. Here we report OpenFold, a fast, memory-efficient, and trainable implementation of AlphaFold2, and OpenProtein-Set, the largest public database of protein multiple sequence alignments. We use OpenProteinSet to train OpenFold from scratch, fully matching the accuracy of AlphaFold2. Having established parity, we assess OpenFold's capacity to generalize across fold space by retraining it using carefully designed datasets.
2021 , Callaway 2022). This implementation has enabled researchers to optimize AlphaFold2's prediction procedure and user experience  (Mirdita, Schütze, et al. 2022 ) and to employ it as a module within novel algorithms, including ones for protein complex prediction  (Baek 2021) , peptide-protein interactions  (Tsaban et al. 2022) , structure ranking  (Roney and Ovchinnikov 2022) , and more (e. g. ,  Baltzis et al. 2022 , Bryant et al. 2022 , Wayment-Steele et al. 2022). In spite of its outstanding utility, the official AlphaFold2 implementation omits code for the model's complex training procedure as well as the computationally expensive training data required to run it. This makes it difficult to i) investigate AlphaFold2's learning behavior and sensitivity to changes in data composition and model architecture and ii) create variants of the model to tackle new tasks.
Given the success of AlphaFold2, its many novel components are likely to prove useful for tasks beyond protein structure prediction. For instance, retraining AlphaFold2 using a dataset of protein-protein complexes resulted in Al-phaFold2-Multimer  (Evans et al. 2022) , the state of the art model for predicting structures of protein complexes. Until recently, however, this capability has been exclusive to DeepMind. To address this shortcoming, we developed OpenFold, a trainable open-source implementation of AlphaFold2, and OpenProteinSet, a database of five million deep and diverse MSAs that removes one of the most significant computational barriers-millions of CPU-hours-to training new protein models at the scale of AlphaFold2. We trained OpenFold from scratch using OpenProteinSet, matching AlphaFold2 in prediction quality.
Apart from new training code and data, OpenFold has several advantages over AlphaFold2: (i) it runs up to three times faster on most proteins, (ii) it uses less memory, allowing prediction of extremely long proteins and multi-protein complexes on a single GPU, and (iii) it is implemented in PyTorch  (Paszke et al. 2019) , the most widely used machine learning framework (AlphaFold2 uses Google's JAX  (Bradbury et al. 2018) ). As such, OpenFold can be readily used by the widest community of developers and interfaces with a rich ecosystem of existing machine learning software  (Rasley et al. 2020 , Charlier et al. 2021 , Falcon et al. 2019 , Charlier et al. 2021 , Dao et al. 2022). We used OpenFold to understand how the model learns to fold proteins, focusing on the geometric characteristics of predicted structures during intermediate stages of training, and identified multiple distinct phases of behavior.
Specifically, by analyzing predicted structures at multiple resolutions and decomposing them into secondary and tertiary elements, we found that OpenFold learns spatial dimensions, secondary structure elements, and tertiary scales in a staggered manner. Next, taking advantage of our discovery that ~90% of model accuracy can be achieved in ~3% of training time, we retrained OpenFold multiple times on specially 2 elided versions of the training set to quantify its ability to generalize to unseen protein folds. Surprisingly, we found the model highly robust even to large elisions of fold space, but its capacity to generalize varied based on the spatial extent of protein fragments and folds. We observed even stronger performance when training the model on more diverse but smaller datasets, some as small as 1,000 experimental structures.
Newly trained OpenFold matches AlphaFold2 in accuracy OpenFold reproduces the AlphaFold2 model architecture in full, without any modifications that could alter its internal mathematical computations. This results in perfect interoperability between OpenFold and AlphaFold2, enabling use of the original AlphaFold2 model parameters within OpenFold and vice versa. To verify that our OpenFold implementation recapitulates all aspects of AlphaFold2 training, we used it to train a new model from scratch. OpenFold/AlphaFold2 training requires a collection of protein sequences, MSAs, and structures. As the AlphaFold2 MSA database has not been publicly released, we generated our own database using the same MSA generation procedure described for AlphaFold2 but substituting newer versions of sequence databases, where available.
Starting from approximately 15 million UniClust30  (Mirdita, Driesch, et al. 2017 ) MSAs, we selected approximately 270,000 diverse and deep MSAs to form a "self-distillation" set; such sets are used to augment experimental training data with high-quality predictions. We predicted protein structures for all MSAs in this set using AlphaFold2 and combined them with approximately 132,000 unique (640,000 non-unique) experimental structures from the Protein Databank (wwPDB Consortium 2018) to form the OpenFold training data set. During training on self-distillation proteins, residues with a low AlphaFold2 confidence score (< 0. 5 pLDDT) were masked. Our validation set consisted of nearly 200 structures from CAMEO  (Haas et al. 2018) , an online repository for continuous quality assessment of protein structure prediction models, drawn over a three-month period ending on January 16, 2022.
To facilitate future development of protein modeling systems, we combined the ~400,000 MSAs in our training set with ~4. 6 million deep MSAs we derived from UniClust30 and released them as OpenProtein-Set, the largest collection of publicly available MSAs. For more details on OpenProteinSet construction procedures, see Appendix B. From our main training run, we selected seven snapshots to form a collection of distinct (but related) models. During prediction time, these models can generate alternate structural hypotheses for the same protein. To further increase the diversity of this collection, we fine-tuned a second set of models that we branched off from the main model. In this second branch, we disabled the model's template pipeline, similar to the procedure used for AlphaFold2. Selected snapshots from this branch were added to the pool of final models, resulting in a total of 10 distinct models.
We take advantage of this fact in our data elision experiments. AlphaFold2 training is broadly split into two phases, an initial training phase and a more computationally intensive fine-tuning phase. In the latter, the size of protein fragments used for training is increased to 384 residues and an additional loss function that penalizes structural violations (e. g. steric clashes) is enabled. By comparing predicted structures between the initial and fine-tuning phases, we find that the second phase has only a modest effect on overall structural quality metrics, even when considering only long proteins greater than 500 residues in length (see Appendix G. 1). Instead, the primary utility of fine-tuning appears to be to resolve violations of known chemical constraints. In our training experiments, this occurs quickly after the beginning of fine-tuning, suggesting that elided fine-tuning runs can be used with minimal impact on prediction quality.
OpenFold can achieve high accuracy using small training sets AlphaFold2 was trained using ~132,000 protein structures from the PDB, the result of decades of painstaking and expensive experimental structure determination efforts. For other molecular systems for which AlphaFold2-style models may be developed, data is far more sparse; e. g. , the PDB contains only 1,664 RNA structures. We wondered whether the high accuracy achieved by AlphaFold2 in fact depended on very large training sets, or if it is possible to achieve comparable performance using less data. Were the latter to be true, it would suggest broad applicability of the AlphaFold2 paradigm to molecular problems. To investigate this possibility, we performed a series of OpenFold training runs in which we used progressively less training data, assessing model accuracy as a function of training set size.
After 20,000 steps (not pictured), the full data model reaches a peak lDDT-Cα of 0. 83, while after 7,000 steps, the 10,000-sample model has already exceeded 0. 81 lDDT-Cα. Although performance gradually degrades as training set size decreases further, we find that all models are surprisingly performant, even ones trained on our smallest subsample of 1,000 protein chains, corresponding to just 0. 76% of the full training set. In fact, this model reaches an lDDT-Cα of 0. 64, exceeding the median lDDT-Cα of 0. 62 achieved at CASP13 by the first AlphaFold, the best performing model at the time. Comparing the accuracies of models trained with and without templates, we find that templates on average contribute little to prediction quality even in the low-data setting. This is consistent with the original AlphaFold2 ablation studies which showed that templates have a minimal effect except when MSAs are shallow or entirely absent.
2022 )), as they are predicted by models trained on the mainly alpha or mainly beta datasets. In the top row of Figure  6C , we show an experimental structure (orange) for Lsi1 3 along with predictions made by the mainly alpha-trained model (purple) and mainly betatrained model (green). In the bottom row we show similar figures for TMED1 4. Predictably, the mainly alpha-trained model accurately predicts the alpha helices of Lsi1 but fails to properly form beta sheets for TMED1 and incorrectly adopts a small alpha helix in part of the structure. The mainly beta-trained model has the opposite problem: its Lsi1 prediction contains poorly aligned helices and an erroneous beta sheet, but TMED1 is reasonably well predicted. Notably, however, neither fails catastrophically. Regions corresponding to the beta sheets of Lsi1 are predicted by the mainly alpha model with approximately the right shape, except that their atomic coordinates are not sufficiently precise to enable DSSP to classify them as beta sheets.
To save memory at training time, which requires storing intermediate computations during the optimization procedure, AlphaFold2 and OpenFold are evaluated on short protein fragments ranging in size from 256 to 384 residues. (orange) and without (blue) new FAPE clamping protocol. Runs using old protocol exhibit substantial instability with two rapidly converging runs, two late converging runs, and one non-converging run. In contrast, all 15 independent runs using the new protocol converge rapidly. Runs using the new protocol also reach high accuracy faster. At inference time, intermediate computations need not be stored, but input sequences can be more than ten times longer than the longest fragments encountered during training. Since the model's memory usage naively grows cubically with input length, inference-time prediction stresses modules that are not necessarily bottlenecks at training time.
Discussion We have developed OpenFold, a complete open-source reimplementation of AlphaFold2 that includes training code and data. By training OpenFold from scratch and matching the accuracy of AlphaFold2, we have demonstrated the reproducibility of the AlphaFold2 model for protein structure prediction. Furthermore, the OpenFold implementation introduces technical advances over AlphaFold2, including markedly faster prediction speed. It is built using PyTorch, the most widely used deep learning framework, facilitating incorporation of OpenFold components in future machine learning models. OpenFold immediately makes possible two broad areas of advances: (i) deeper analyses of the strengths, weaknesses, and learning behavior of AlphaFold2-like models and (ii) development of new (bio)molecular models that take advantage of AlphaFold2 modules.
Regardless, these observations suggest that it may be possible to deliberately simplify other difficult problems in molecular modeling with a learning curriculum in which "toy" models are first trained to predict lower-dimensional projections of target molecules (or more generally, geometric objects) before being tasked to predict their fully realized instantiations. Third, we assessed OpenFold's capacity to learn from training sets substantially reduced in size. Remarkably, we found that even a 100-fold reduction in dataset size (0. 76% models in Figure  6A ) results in models more performant than the first version of AlphaFold. Stated differently, the architectural advances introduced in AlphaFold2 enable it to be 100x more data efficient than its predecessor, which at the time of its introduction set a new state of the art.
These results demonstrate that architectural innovations can have a more profound impact on model accuracy than larger datasets, particularly in domains where data acquisition is costly or time-consuming, as is often the case in (bio)molecular systems. However, it merits noting that AlphaFold2 in general learns MSA-structure, not sequence-structure, relationships. MSAs implicitly encode a substantial amount of structural knowledge, as evidenced by early co-evolution-based structure prediction methods which were entirely unsupervised, making no use of experimental structural data  (Marks et al. 2011 , Sułkowska et al. 2012). Hence, the applicability of the AlphaFold2 architecture to problems that do not exhibit a co-evolutionary signal remains undemonstrated. Our data elision results can be interpreted in light of recent work on large transformerbased language models that has revealed broadly applicable "scaling laws" that predict model accuracy as a simple function of model size, compute utilized, and training set size et al.