dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). MS data demonstrating the genome wide expression of AltProts is still limited to few species  (Fabre et al. , 2021). The roles of only few alternative proteins, less than 50 across all species, have been characterized to date  (Plaza et al. , 2017b; Wright et al. , 2021). The alternative proteins, which function has been determined, seem to be involved in a wide range of key biological processes  (Plaza et al. , 2017b; Wright et al. , 2021). Due to their large spectrum of functions, alternative proteins represent an attractive new repertoire of molecules for drug development and agricultural applications. In an effort to assess the pervasive production of alternative proteins in the model organism Drosophila melanogaster, we describe here the development of a deep peptidomics workflow combining different protein extraction methods, small protein enrichment steps, state of the art mass spectrometry and optimized bioinformatics analysis using the well curated OpenProt database.
The raw files were searched against the OpenProt fasta Drosophila melanogaster database (release 1. 6, Altprots, Isoforms and Refprots). For the identification of RefProts, default MaxQuant settings were used (1% FDR both at the protein and PSM levels). Regarding AltProts identification, a minimum score of 70 was set for both modified and unmodified peptides (corresponding to the first quartile of the distribution of the score of RefProts from an analysis of the raw files with MaxQuant default settings). Candidates were filtered to obtain an FDR of 1% at the peptide level. Because alternative proteins are generally shorter than canonical proteins no FDR was set at the protein level and no filter was applied to the number of peptides per protein. A minimum sequence coverage of 70 % of the peptide sequence was required for alternative proteins identification.
MSMS spectra were manually inspected by two independent operators. Peptides matching both a novel predicted protein and a RefProt were discarded. As implemented in OpenProt  (Brunet et al. , 2019) , peptide matching two AltProts, two novel Isoforms or an AltProt and a novel Isoform were assigned to both proteins in each case. For quantification, the match between runs and iBAQ modules of MaxQuant were enabled. Quantitative comparison between AltProts and RefProts were performed on samples from the high pH reverse phase experiments only (Protein extraction and alternative protein enrichment protocol number 2, and data from  (Müller et al. , 2020) ). As iBAQ represents an approximation of the absolute abundance of a protein  (Fabre et al. , 2014)  and given the low number of observable peptides for AltProts, we considered that an AltProt was more (or less) abundant than its corresponding RefProt if the ratio between their iBAQ values was at least 10-fold different.
Detection of the AltProt and RefProt domains The RefProt domains have been collected from the InterPro database  (Blum et al. , 2021a). All domains identified on UniProtKB reviewed proteins of Drosophila melanogaster (Proteome identifier: UP000000803) have been recovered using the EBI REST API. The domains on AltProt sequences have been identified using InterProScan  (Jones et al. , 2014 )(v5. 52-87. 0) looking for signatures in the Pfam database  (Mistry et al. , 2021). The signatures with an e-value lower than 10 -5 have been selected. Pfam identifiers have been mapped to InterPro accessions using the InterPro cross-references collected through the EBI REST API. Detection of the short linear motifs (SLiMs) Classes of SLiMs have been downloaded from the Eukaryotic Linear Motif (ELM) database  (Kumar et al.
, 2020). Classes with a pattern probability lower than 0. 01 and having at least one true positive instance detected in D. melanogaster in ELM database have been selected. The short linear motifs (SLiMs) have then been detected in the disordered regions of the AltProts using the IUPred2A  (Mészáros et al. , 2018a)  and the Short Linear Motif Probability tool (SLiMProb) of SLiMSuite  (Edwards et al. , 2020) , using the following SLiMProb parameters: iumethod = long, iucut = 0. 2, minregion = 5.
Genome wide identification of Alternative proteins in Drosophila melanogaster In order to identify new alternative proteins in Drosophila melanogaster we developed a customized peptidomics workflow (Figure  1 ). We used a combination of protein extraction and small proteins enrichment protocol as it was previously shown to increase the number of AltProts identified by mass spectrometry  (Ma et al. , 2016; Cardon et al. , 2020). Extensive fractionation, using high pH reverse phase chromatography, as well as specific enrichment of short proteins, through SDS-PAGE, ultrafiltration, acid precipitation and reverse phase chromatography, was employed to retrieve AltProts from adult flies, 0-24h embryos and S2 cells (Figure  1 ). We also re-analyzed MS data available in public repositories. In total, 1,068 MS files were analyzed using optimized MaxQuant parameters and the OpenProt predicted AltProts database (Figure  1 ).
In total, 401 AltProts as well as 8,615 RefProts (including 267 RefProts containing less than 100 amino acids annotated in UniProtKB) were identified (Figure  2  and Supplementary table  1  and 2 ). The identification scores obtained for the AltProts were similar to the ones measured for a typical proteomics analysis (median Andromeda score of 98. 52 for AltProts and 101. 97 for RefProts) (Supplementary Figure  1A ). The majority of the AltProts identified here are short proteins (88. 8% of AltProts are less than 150 amino acids) (Supplementary Figure  1B ). Comparing the AltProts identified to the ones with MS evidence in OpenProt and a recent article  (Wang et al. , 2022) , only 2 were common to the three datasets, 29 were found in at least two datasets and 374 new AltProts were identified in this study (Supplementary Figure  1C ).
The low overlap between the datasets might be explained by the different sample types and extraction and fractionation protocols used  (Cardon et al. , 2020). Amongst the 401 non-annotated proteins identified, 30 were new Isoforms (Figure  2 ). As defined in OpenProt (https://www. openprot. org/), we refer here as Isoform (or novel Isoform) any non-annotated proteins that share some homology with a RefProt (either partially overlapping coding sequences, although only Isoform unique peptides are used for their identification). Next, we looked at the RNA types and regions from which AltProts are produced (Figure  2 ). We used a recently suggested nomenclature  (Mudge et al. , 2021)  to refer to the types of ORFs encoding the AltProts (Figure  2 ). Surprisingly, whereas pioneering studies identified non-coding RNA or untranslated regions of mRNAs as main sources of AltProts  (Plaza et al.
Analysis using the TMHMM -2. 0  (Krogh et al. , 2001)  and SignalP -5. 0 (Almagro Armenteros et al. , 2019) software identified possible transmembrane domains and signal peptides for 33 and 9 AltProts, respectively. Interestingly, the AltProt IP_1410397 encoded by the host gene CG15784 had both a signal peptide and a transmembrane domain predicted (both with probabilities > 0. 8) in the first 30 amino acids of its sequence (Supplementary Figure  9 ). We next looked for the presence of short linear motifs (SLiMs) in the AltProts identified. SLiMs are functional short stretches of protein sequence that are generally involved in protein-protein interactions  (Hraber et al. , 2020). A total of 684 SLiMs were mapped on 191 AltProts (Figure  5C -D and Supplementary table  4 ). Most of the SLiMs retrieved belong to the post-translational modification sites (MOD) (enriched in isoforms, Benjamini-Hochberg adjusted p-value = 3.
, 2020). Next, the IUPred2A software was used to predict disordered regions within AltProts. Around 50% of the AltProts identified in our study contained one or more predicted disordered region (Supplementary Figure  11 ). A higher proportion of Isoforms (70%) and AltProts produced from uORFs (58. 7%) and intORFs (55. 9%) tend to have disordered regions compared to lncRNA-ORFs (38. 5%) or dORFs (26. 1%) (Supplementary Figure  10 ). This is in agreement with a recent report in plants, which also predicted that numerous nonannotated short proteins might contain disordered regions, transmembrane domains or signal peptides  (Fesenko et al. , 2021). DeepLoc  (Almagro Armenteros et al. , 2017)  was used to predict possible subcellular localization of Altprots. A potential localization was assigned to 79 out of 401 with a probability higher than 0.
, 2014) , were used to compare the abundance of AltProts with the abundance of their corresponding RefProts in total lysates and high pH reverse phase fractionation experiments (Figure  7A  and 7B ). Out of the 39 pairs of AltProts/RefProts for which iBAQ values were measured, 22 did not show any difference in abundance between AltProts and RefProts expression levels (less than 10-fold difference between the iBAQ values), whereas 11 AltProts were more abundant (Figure  7A  and 7B ). Only 6 RefProts were more abundant than their corresponding AltProts (Figure  7A  and 7B ). This trend was observed in two independent datasets (Supplementary figure  13B ) and we did not observe any bias based on the length of the AltProts (Supplementary figure  14 ). These data reveal that, in several cases, alternative proteins are actually the main protein produced from their corresponding genes.
These data suggest that it might be worth reconsidering the phenotypes observed in certain mutants in D. melanogaster as they might be mediated by the mutation/deletion of the alternative protein rather than the canonical one. Finally, several AltProts were identified in only specific developmental stages or upon heat shock, implying that their expression is finely tuned during D. melanogaster development or under stress conditions  (Figure 7C and 7D). These proteins might have important functions during development or heat shock response, hence requiring further functional investigation. Alternative proteins were then enriched from the total protein pool and digested with trypsin (or other enzymes). The resulting peptides were injected on different mass spectrometry platforms and the generated MS data, as well as datasets available from public repositories, were analyzed using MaxQuant with the OpenProt database.
Acknowledgements This work has been supported by the  Fondation ARC  pour la recherche sur le cancer. B. F. is funded by  Biotechnology and Biological Science Research Council  (ref:  BB/L002817/1 ) and a long term EMBO fellow ( ALTF 1204-2015 ) cofounded by  Marie Curie Actions  ( LTFCOFUND2013 ,  GA-2013-609409 ). S. A. C. is funded by a  Fondation pour la Recherche Médicale  fellowship (FDT202106013072). D. K. is funded by  Biotechnology and Biological Science Research Council  (ref:  BB/L002817/1 ). The work was funded in part by grants from the  Région Occitanie, European funds (Fonds Européens de Développement Régional, FEDER) ,  Toulouse Métropole , and the  French Ministry of Research  with the  Investissement d'Avenir Infrastructures Nationales en Biologie et Santé program  ( ProFI, Proteomics French Infrastructure  project,  ANR-10-INBS-08 ).
Highlights  The HEROHE Challenge was organized to promote the development of computer-aided diagnosis tools to assess human epidermal growth factor receptor 2 status in invasive breast cancer samples stained with hematoxylin and eosin.  Two whole slide images datasets of invasive breast cancer tissue samples were compiled to be used as training (359 cases) and test (150 cases) datasets.  A total of 21 methods were developed, submitted, and ranked according to their performance on the test dataset. A comparative analysis was performed and the top-6 ranked for the  1 score are described.  The large majority of the methods rely on pre-trained convolution neural networks. More than half of these methods (including 5 of the top-6) result from the combination of more than one network.
In this work, a deep convolutional neural network (CNN) based on a residual network (ResNet  [28] ) architecture was developed to predict 19 biomarkers, including ER, PgR, and HER2 from tissue morphology. For these 3 biomarkers, the area under the receiver operating characteristic curve (AUC) was 0. 80, 0. 75, and 0. 74, respectively. The data originated from a single institution (Vancouver General Hospital) and included only TMA images from 5356 patients, rather than WSI, representing two important limitations. Ultimately, the system was able to predict biomarker expression with noninferiority to traditional IHC assay. More recently, Naik and colleagues  [29]  developed a multiple instance learning-based deep neural network to predict the same molecular biomarkers from H&E-stained WSI. The algorithm based on a ResNet50 was trained in a multi-country dataset of 3474 patients (Australian Breast Cancer Tissue Bank and The Cancer Genome Atlas), and achieved an AUC of 0.
92, 0. 81, and 0. 78, for ER, PgR, and HER2, respectively. Following the same rationale, Kather and co-workers  [30]  developed a deep learning model based on ShuffleNet  [31]  to predict molecular alterations in 14 of the most common solid tumor types, including breast cancer. The system trained on The Cancer Genome Atlas dataset was able to infer from histology images alone at least one mutation from all except one tested tumor type. In breast cancer, ER, PgR and HER2 subtype could be predicted with an AUC of 0. 82, 0. 74, and 0. 75, respectively.
HEROHE challenge organization The HEROHE Challenge website was hosted on the Grand Challenge servers with the domain https://ecdp2020. grand-challenge. org/. The Grand Challenge is one the largest platforms on medical imaging challenges, counting, at the time of writing, with more than 40,000 users. Hosting HEROHE at the Grand Challenge website allowed for an easy setup while maximizing the number of researchers reached. The challenge was also advertised through the social media networks and official webpage of the ECDP2020, and monetary prizes were awarded to the three best-performing methods. Unlike previous challenges, where IHC images were part of the training and testing datasets, the goal here was to classify HER2 status directly from the morphological features present on the H&E stained images.
Thus, the training dataset consisted of 359 WSI of invasive BC tissue samples stained only with H&E and the corresponding image-wise ground truth classification based on IHC and ISH. The cases did not include annotations such as the location of the invasive carcinoma and no IHC or ISH slides were provided. The ground truth originated from IHC and ISH tests, resulting in a binary classification (negative or positive). Considering the IHC HER2 scores, the dataset had 43 (12%) cases scored of 0, 47 (13%) scored of 1+, 230 (64%) score of 2+, and 39 (11%) scored of 3+. One case with a score of 1+ had HER2 amplification by ISH, being classified as HER2 positive. In cases with a score of 2+, 126 cases were ISH-negative and 104 ISH-positive. Hence, the dataset contained 144 HER2 positive cases (40%) and 215 HER2 negative cases (60%).
There were 358 female and 1 male cases, with ages between 24 and 92 (median 58 years old). Cases originated in 22 different laboratories and all ISH tests were performed at Ipatimup Diagnostics (national reference center for HER2). Cases with HER2 heterogeneity were not included in the dataset. All cases were classified by two experienced pathologists (CE and AP) according to the latest American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines for BC, scanned at Ipatimup Diagnostics in a 3D Histech Pannoramic 1000 digital scanner at 20x magnification and saved in the MIRAX file format. On October 1 st , 2019, the HEROHE Challenge website and training dataset were released. The test dataset was released on January 6 th , 2020. In total 150 WSI correspondent to 150 cases were acquired following the same conditions of the training dataset, including the proportion of positive and negative cases (test dataset distribution was not previously known by the participants).
Among the 150 WSI of the test dataset, 19 (13%) with a score of 0 on the IHC test, 18 (12%) with a score of 1+, 85 (57%) with a score of 2+, 27 (18%) with a score of 3+ and 1 case without IHC score. In cases with a score of 2+, 53 cases were ISH-negative and 32 ISH-positive. The case without IHC score was HER2positive by ISH. Hence, the dataset contained 60 HER2 positive cases (40%) and 90 HER2 negative cases (60%). There were 149 female and 1 male cases, with ages between 33 and 93 (median of 57 years old) from 17 different pathology laboratories. All cases from test and training datasets originate from different patients to ensure independence between datasets. We were also able to trace 116 cases, from both the training and the test datasets, that showed positive (score of 3+) or negative (score of 0 or 1+) results by IHC and the corresponding ISH results obtained either by an internal or external quality control protocol.
In these cases, there was only one false-negative case by IHC (mentioned above), providing a sensitivity of 0. 98, a specificity of 1. 00, a positive predictive value of 1. 00, and a negative predictive value of 0. 98 for the IHC analysis. To participate and be eligible for the Challenge's prizes at least one element of each competing team should be registered to ECDP2020 and submitted, until January 28 th , 2020, the methods code, the test dataset prediction (hard and soft predictions), and a short method description. The ECDP2020 registration requirement was later removed from the challenge rules due to the cancellation of the congress.
Team Macaroon Team Macaroon employed a two-stage method to solve the problem. In stage A, a ResNet34 model, pre-trained on the CAMELYON16  [34]  challenge datasets, was used for training a patch-based (256 by 256 pixels) classification model to differentiate normal tissue patches from tumor patches (see  [44]  for training details). A probability map, PM_A, results from each WSI. In stage B, each original WSI was down-sampled (ratio 1:2) and a sliding window split it into 256 by 256 pixels patches to be classified by the model from stage A. Potential tumor patches were extracted and the HER2 status information of the WSI was added to each patch. The resulting dataset was used for training a second ResNet34 model aiming to classify tumor patches as HER2 positive or HER2 negative. Model B was used for generating the final probability map of the WSI (denoted as PM_B).
Team MITEL A five-stage procedure was used by team MITEL (see Figure  2 ). The full method is described in  [45]. A preprocessing step was implemented where each WSI was first downsampled (ratio=1:2) and tiles were created by a sliding window of 512 by 512 pixels. Only tiles with an average grey level of <85% were retained. In the second stage, Tumor Detection, tiles were classified as tumor or normal by a DenseNet201  [46]. The model used was pre-trained on ImageNet  [47]  and then fine-tuned on the BACH  [35]  dataset for the tumor classification task. Tiles classified as normal tissue were discarded while the others were used in the following stage. In the third stage, HER2 classification, the remaining tiles were fed to a ResNet152 (optimized for precision), and the model was trained to predict the probability of a given tile being from a HER2 positive WSI.
Team Piaz The team Piaz used a four-stage procedure (https://github. com/IAmS4n/HEROHE) to classify each WSI in a multi-instance learning fashion (see Figure  3 ). In stage one, the Shannon entropy of the WSI was computed to identify the most informative regions, and then a threshold, based on the method of minimum value in histogram  [48] , was found to make a tissue mask. Next, 256 random patches of 222 by 222 pixels were extracted from the tissue mask's valid regions at a maximum resolution. In the second stage, an EfficientNet-B0  [49] , pre-trained on the BACH challenge  [35]  dataset, was used and retrained on the HEROHE dataset to extract a 64-dimensional feature array for each patch. The features were extracted using a max-pooling over the last CNN layer of EfficientNet. In addition, there was a batch-norm layer followed by an absolute operation after each max-pooling.
Team Dratur The team Dratur used a method consisting of two parallel tracks (@20x and @5x tracks) to classify each WSI (see Figure  4 ). Tumor regions were manually annotated in 3DHistech CaseViewer and exported to TIFF file format with 3DHistech SlideConverter. After preprocessing for brightness adaption the TIFF files were sliced with a sliding window procedure generating 256 by 256 pixel tiles at the original 20x magnification and 256 by 256 pixel tiles at 5x magnification. Tiles with less than 50% of tissue pixels were discarded. A sample of tissue tiles was manually grouped for vital invasive carcinoma and non-tumor (including ductal carcinoma in situ, DCIS, in the @5x track). Two EfficientNets B4 were trained to enrich vital invasive tumors in both tracks. Strong and complex data augmentations were applied in the training of all convolutional neural networks, including the modification of hue and saturation, the addition of salt and pepper artifacts, color noise, or block artifacts using the imgaug library (https://github.
com/aleju/imgaug) as well as affine augmentations from the Keras library. HER2 positive and negative cases were split into 5 partitions keeping the class balance in each partition. EfficientNets B4 and B2 were then trained using a 5-fold cross-validation procedure to predict the HER2 status. The resulting soft predictions were fed into a small dense convolutional network (two hidden layers with 32 and 16 nodes, L2 regularization, and drop out) trained with a 3-fold split for cross-validation. The resulting soft-predictions were tested against the training dataset ground-truth and a threshold of 0. 47 was defined to generate the hard prediction for each WSI resulting in the correct classification of 86. 39% of the training WSI, compared to a correct classification of 85. 27% at a threshold of 0.
5. The team IRISAI used a two-stage model to solve the problem (see Figure  5 ). In the first stage, a U-Net  [50]  model was trained from scratch on slides at 5x magnification, to segment each WSI into "cellular" and "non-cellular" regions. Training images for this task resulted from an interactive set of annotations and corrections created in the DeePathology STUDIO software. In the second stage, 900,000 image patches of size 256x256 pixels were extracted from the cellular regions at 20x magnification and used as training dataset. A patch was considered cellular if at least 95% of its pixels were predicted as cellular by the U-Net network of stage one. Patches for the weakly supervised task were labeled by assigning to each one a value according to the corresponding WSI with a smoothing of 10% to accommodate for patch selection errors.
In other words, patches originating from a positive WSI were labeled as 0. 9 while those originating from a negative WSI were labeled as 0. 1. Standard data augmentation was then applied to the resulting dataset and a Resnet50 classifier, pre-trained on ImageNet  [47] , was trained to predict the patch-level labels using an Adam optimizer with default parameters. Finally, the WSI-level HER2 score was computed by splitting it into tiles and measuring the ratio of cellular tiles in that slide that had an output of above 0. 5 from the Resnet50 classifier.