🔴 Goal: The goal of the competition is to predict the function of a set of proteins based on their amino acid sequences and other data.\n
⚪ Importance: The accurate assignment of biological function to proteins is important for understanding life at the molecular level, discovering how cells, tissues, and organs work, and developing new drugs and therapies for various diseases.
⚪ Context: Proteins are large molecules composed of 20 types of building-blocks known as amino acids. The human body makes tens of thousands of different proteins, and each protein is composed of dozens or hundreds of amino acids that are linked sequentially.
⚪ Challenges: Assigning function to any specific protein can be made difficult due to the multiple functions many proteins have, along with their ability to interact with multiple partners. There are still many challenges in this field, which are driven by ambiguity, complexity, and data integration.
⚪ Host: The Function Community of Special Interest (Function-COSI) is hosting the competition. Function-COSI brings together computational biologists, experimental biologists, and biocurators who are dealing with the important problem of gene and gene product function prediction.
⚪ Co-organizers: The Kaggle competition is co-organized by academic experts from Iowa State University, Northeastern University, University of Padova, and UniProt.
⚪ Acknowledgments: The competition is supported by Iowa State University, Northeastern University, University of Padova, UniProt, and the International Society for Computational Biology.
🔴 The Gene Ontology (GO) is a concept hierarchy that describes the biological function of genes and gene products at different levels of abstraction (Ashburner et al., 2000). It is a good model to describe the multi-faceted nature of protein function.
\n\n⚪ GO is a directed acyclic graph. The nodes in this graph are functional descriptors (terms or classes) connected by relational ties between them (is_a, part_of, etc.). For example, terms 'protein binding activity' and 'binding activity' are related by an is_a relationship; however, the edge in the graph is often reversed to point from binding towards protein binding. This graph contains three subgraphs (subontologies): Molecular Function (MF), Biological Process (BP), and Cellular Component (CC), defined by their root nodes. Biologically, each subgraph represent a different aspect of the protein's function: what it does on a molecular level (MF), which biological processes it participates in (BP) and where in the cell it is located (CC).
⚪ The protein's function is therefore represented by a subset of one or more of the subontologies. These annotations are supported by evidence codes, which can be broadly divided into experimental (e.g., as documented in a paper published by a research team of biologists) and non-experimental. Non-experimental terms are usually inferred by computational means. We recommend you read more about the different types of GO evidence codes.
\n\n🔴 In this competition uses experimentally determined term-protein assignments as class labels for each protein. That is, if a protein is labeled with a term, it means that this protein has this function validated by experimental evidence. By processing these annotated terms, we can generate a dataset of proteins and their ground truth labels for each term. The absence of a term annotation does not necessarily mean a protein does not have this function, only that this annotation does not exist (yet) in the GO. A protein may be annotated by one or more terms from the same subontology, and by terms from more than one subontology.
\n","metadata":{}},{"cell_type":"markdown","source":"\n#⚪ For the training set, we include all proteins with annotated terms that have been validated by experimental or high-throughput evidence, traceable author statement (evidence code TAS), or inferred by curator (IC). We use annotations from the UniProtKB release of 2022-11-17. The participants are not required to use these data and are also welcome to use any other data available to them.
","metadata":{}},{"cell_type":"markdown","source":"⚪ For the training set, we include all proteins with annotated terms that have been validated by experimental or high-throughput evidence, traceable author statement (evidence code TAS), or inferred by curator (IC). We use annotations from the UniProtKB release of 2022-11-17. The participants are not required to use these data and are also welcome to use any other data available to them.
","metadata":{}},{"cell_type":"markdown","source":"❔ Let's consider each trainig file iteratively.
","metadata":{}},{"cell_type":"markdown","source":"\n##⚪ Gene Ontology: The ontology data is in the file go-basic.obo. This structure is the 2023-01-01 release of the GO graph. This file is in OBO format, for which there exist many parsing libraries. For example, the obonet package is available for Python. The nodes in this graph are indexed by the term name, for example the roots of the three onotlogies are:
subontology_roots = {'BPO':'GO:0008150',\n 'CCO':'GO:0005575',\n 'MFO':'GO:0003674'}\n❔ Let's use obonet package to load this file.
","metadata":{}},{"cell_type":"code","source":"%%time\ngraph = obonet.read_obo(CFG.train_go_obo_path)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:45:35.050717Z","iopub.execute_input":"2023-04-19T00:45:35.051631Z","iopub.status.idle":"2023-04-19T00:45:43.855204Z","shell.execute_reply.started":"2023-04-19T00:45:35.05158Z","shell.execute_reply":"2023-04-19T00:45:43.854282Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Number of nodes.
","metadata":{}},{"cell_type":"code","source":"print(f\"Number of nodes: {len(graph)}\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:45:43.856444Z","iopub.execute_input":"2023-04-19T00:45:43.857109Z","iopub.status.idle":"2023-04-19T00:45:43.863605Z","shell.execute_reply.started":"2023-04-19T00:45:43.857067Z","shell.execute_reply":"2023-04-19T00:45:43.861503Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Number of edges.
","metadata":{}},{"cell_type":"code","source":"print(f\"Number of edges: {graph.number_of_edges()}\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:45:43.86498Z","iopub.execute_input":"2023-04-19T00:45:43.865308Z","iopub.status.idle":"2023-04-19T00:45:44.015859Z","shell.execute_reply.started":"2023-04-19T00:45:43.865259Z","shell.execute_reply":"2023-04-19T00:45:44.014856Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"🔴 To display a graph, you need to focus on a specific term, let's take term GO:0034655 as an example.
❔ Lookup nucleobase-containing compound catabolic process node properties (term GO:0034655).
❔ Let's plot DAG for the term GO:0034655.
","metadata":{}},{"cell_type":"code","source":"plot_dag(graph, term, radius=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{},"attachments":{"e1e351c2-11c5-4c7c-9bbf-fa2ee6f003d8.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"❔ Now let's look how's full DAG looks like for the selected term. To do that just increase value of the radius parameter. radius - responsible include all neighbors of distance ≤ radius from n.
⚪ In the first graph, only the nodes are connected by relational ties between them, that is, which are located in is_a. But the second one shows a complete graph in which you can see how the connection will look with a non-peripheral relationship between all the nodes in the graph.
🔴 Let's look at another term. The term GO:0044270 taken as an example.
❔ Lookup cellular nitrogen compound catabolic process node properties (term GO:0044270).
❔ Let's plot DAG for the term GO:0044270 (run this cell by yourself to interact with graph).
","metadata":{}},{"cell_type":"code","source":"plot_dag(graph, term, radius=1)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{},"attachments":{"9852681d-a05c-4545-b207-b740915079c5.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"❔ Now let's look how's full DAG looks like for the selected term. To do that just increase value of the radius parameter. radius - responsible include all neighbors of distance ≤ radius from n.
⚪ These graphs are much more readable. Feel free to experiment with other terms.
","metadata":{}},{"cell_type":"markdown","source":"\n##⚪ Training sequences: train_sequences.fasta contains the protein sequences for the training dataset.
⚪ This files are in FASTA format, a standard format for describing protein sequences. The proteins were all retrieved from the UniProt data set curated at the European Bioinformatics Institute.
\n\n⚪ The header contains the protein's UniProt accession ID and additional information about the protein. Most protein sequences were extracted from the Swiss-Prot database, but a subset of proteins that are not represented in Swiss-Prot were extracted from the TrEMBL database. In both cases, the sequences come from the 2022_05 release from 14-Dec-2022. More information can be found here.
\n\n⚪ The train_sequences.fasta file will indicate from which database the sequence originate. For example, sp|P9WHI7|RECN_MYCT in the FASTA header indicates the protein with UniProt ID P9WHI7 and gene name RECN_MYCT was taken from Swiss-Prot (sp). Any sequences taken from TrEMBL will have tr in the header instead of sp. Swiss-Prot and TrEMBL are both parts of UniProtKB.
⚪ This file contains only sequences for proteins with annotations in the dataset (labeled proteins). To obtain the full set of protein sequences for unlabeled proteins, the Swiss-Prot and TrEMBL databases can be found here.
","metadata":{}},{"cell_type":"markdown","source":"🔴 To read and analyze the protein sequences from the train_sequences.fasta file, we can use the Biopython package.
❔ Let's count the number of sequences.
","metadata":{}},{"cell_type":"code","source":"sequences = SeqIO.parse(CFG.train_seq_fasta_path, \"fasta\")\nnum_sequences = sum(1 for seq in sequences)\n\nprint(\"Number of sequences:\", num_sequences)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:47:55.432466Z","iopub.execute_input":"2023-04-19T00:47:55.432926Z","iopub.status.idle":"2023-04-19T00:47:57.512725Z","shell.execute_reply.started":"2023-04-19T00:47:55.432885Z","shell.execute_reply":"2023-04-19T00:47:57.511484Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Let's plot the length distribution of the protein sequences.
","metadata":{}},{"cell_type":"code","source":"sequences = SeqIO.parse(CFG.train_seq_fasta_path, \"fasta\")\n\n# get the length of each sequence\nlengths = [len(seq) for seq in sequences]\n\nfig = px.histogram(x=lengths, nbins=1000, color_discrete_sequence=['goldenrod'])\nfig.update_layout(\n title={\n 'text': \"Distribution of protein sequence lengths\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'\n },\n xaxis_title=\"Sequence length\", yaxis_title=\"Count\"\n)\n\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:47:58.72728Z","iopub.execute_input":"2023-04-19T00:47:58.727685Z","iopub.status.idle":"2023-04-19T00:48:02.437439Z","shell.execute_reply.started":"2023-04-19T00:47:58.72765Z","shell.execute_reply":"2023-04-19T00:48:02.435916Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Let's check to what value 99% of the values are located.
","metadata":{}},{"cell_type":"code","source":"np.percentile(lengths, 99)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:08.132821Z","iopub.execute_input":"2023-04-19T00:48:08.13327Z","iopub.status.idle":"2023-04-19T00:48:08.160086Z","shell.execute_reply.started":"2023-04-19T00:48:08.133223Z","shell.execute_reply":"2023-04-19T00:48:08.158677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⚪ This also means that only 1% of the data is present after the value 2817.
","metadata":{}},{"cell_type":"markdown","source":"❔ Let's calculate the amino acid composition of each protein sequence. Amino acid composition is the frequency distribution of amino acids in a protein sequence. It can provide valuable information about the protein's structure and function.
","metadata":{}},{"cell_type":"code","source":"records = SeqIO.parse(CFG.train_seq_fasta_path, \"fasta\")\n\n# create a list of all amino acids in the sequences\naa_list = [aa for record in records for aa in record.seq]\n\n# count the frequency of each amino acid\naa_count = Counter(aa_list)\n\nfig = px.bar(\n x=list(aa_count.values()), y=list(aa_count.keys()),\n color_discrete_sequence=['darkslateblue'],\n orientation='h', height=700\n)\nfig.update_layout(\n title={\n 'text': \"Amino Acid Composition\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'\n },\n xaxis_title=\"Frequency\", yaxis_title=\"Amino Acid\"\n)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:10.153746Z","iopub.execute_input":"2023-04-19T00:48:10.154196Z","iopub.status.idle":"2023-04-19T00:48:20.89312Z","shell.execute_reply.started":"2023-04-19T00:48:10.154157Z","shell.execute_reply":"2023-04-19T00:48:20.89179Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"⚪ Here are a few observations that can be made from the obtained frequency values:
\n\n*The most common amino acids in this dataset are leucine (L), serine (S), alanine (A), and glycine (G). These amino acids are known to be abundant in proteins and play important roles in protein structure and function.
\n*The least common amino acids in this dataset are cysteine (C), methionine (M), tryptophan (W), and histidine (H). These amino acids are typically less abundant in proteins, but they can be important for specific functions, such as catalysis, metal binding, or protein-protein interactions.
\n*The presence of the amino acid selenocysteine (U) in the dataset suggests that some of the proteins may be selenoproteins, which contain selenium in the form of selenocysteine instead of cysteine.
\n*The presence of ambiguous amino acids (X, B, Z) and rare amino acids (O, U) in the dataset suggests that some of the sequences may be incomplete or contain errors.
","metadata":{}},{"cell_type":"markdown","source":"\n##⚪ Labels: train_terms.tsv contains the list of annotated terms (ground truth) for the proteins in train_sequences.fasta.
The first column indicates the protein's UniProt accession ID.
\n*The second is the GO term ID.
\n*The third indicates in which ontology the term appears (BPO, CCO or MFO). BPO, CCO, and MFO are abbreviations for different categories of gene ontology terms. BPO: Biological Process Ontology, which describes biological processes, functions, and pathways. CCO: Cellular Component Ontology, which describes the components of a cell or its extracellular environment. MFO: Molecular Function Ontology, which describes the biochemical activities or capabilities of proteins and other molecules. These categories are used in gene ontology to classify genes and gene products based on their biological roles and functions. By using these categories, researchers can better understand the functions and interactions of different genes and gene products within a biological system.
⚪ Load the train terms dataframe.
","metadata":{}},{"cell_type":"code","source":"train_terms_df = pd.read_csv(CFG.train_terms_path, sep=\"\\t\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:25.763353Z","iopub.execute_input":"2023-04-19T00:48:25.764151Z","iopub.status.idle":"2023-04-19T00:48:29.276458Z","shell.execute_reply.started":"2023-04-19T00:48:25.764107Z","shell.execute_reply":"2023-04-19T00:48:29.274985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Let's look how's data looks like.
","metadata":{}},{"cell_type":"code","source":"train_terms_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:29.278532Z","iopub.execute_input":"2023-04-19T00:48:29.279013Z","iopub.status.idle":"2023-04-19T00:48:29.303682Z","shell.execute_reply.started":"2023-04-19T00:48:29.27897Z","shell.execute_reply":"2023-04-19T00:48:29.302328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Display main information about dataframe columns.
","metadata":{}},{"cell_type":"code","source":"train_terms_df.describe()","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:29.305127Z","iopub.execute_input":"2023-04-19T00:48:29.305503Z","iopub.status.idle":"2023-04-19T00:48:31.333998Z","shell.execute_reply.started":"2023-04-19T00:48:29.305467Z","shell.execute_reply":"2023-04-19T00:48:31.332498Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Now let's plot pie distribution of aspect values.
","metadata":{}},{"cell_type":"code","source":"aspect_counts = train_terms_df.aspect.value_counts()\n\nfig = px.pie(values=aspect_counts.values, names=aspect_counts.index)\nfig.update_traces(textposition='inside', textfont_size=14)\nfig.update_layout(\n title={\n 'text': \"Pie distribution of aspect values\",\n 'y':0.95,\n 'x':0.5,\n 'xanchor': 'center',\n 'yanchor': 'top'\n },\n legend_title_text='Aspect:'\n)\nfig.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-04-19T00:48:31.337627Z","iopub.execute_input":"2023-04-19T00:48:31.338409Z","iopub.status.idle":"2023-04-19T00:48:32.314566Z","shell.execute_reply.started":"2023-04-19T00:48:31.338365Z","shell.execute_reply":"2023-04-19T00:48:32.313354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n##⚪ Taxonomy: train_taxonomy.tsv contains the list of proteins and the species to which they belong, represented by a \"taxonomic identifier\" (taxon ID) number. The first column is the protein UniProt accession ID and the second is the taxon ID. More information about taxonomies can he found here.
⚪ Load the train taxonomy dataframe.
","metadata":{}},{"cell_type":"code","source":"train_taxonomy_df = pd.read_csv(CFG.train_taxonomy_path, sep=\"\\t\")","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:35.040078Z","iopub.execute_input":"2023-04-19T00:48:35.041217Z","iopub.status.idle":"2023-04-19T00:48:35.169327Z","shell.execute_reply.started":"2023-04-19T00:48:35.041166Z","shell.execute_reply":"2023-04-19T00:48:35.168034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Let's look how's data looks like.
","metadata":{}},{"cell_type":"code","source":"train_taxonomy_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:35.48919Z","iopub.execute_input":"2023-04-19T00:48:35.489587Z","iopub.status.idle":"2023-04-19T00:48:35.499978Z","shell.execute_reply.started":"2023-04-19T00:48:35.489545Z","shell.execute_reply":"2023-04-19T00:48:35.498617Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"❔ Display dataframe length.
","metadata":{}},{"cell_type":"code","source":"len(train_taxonomy_df)","metadata":{"execution":{"iopub.status.busy":"2023-04-19T00:48:37.244382Z","iopub.execute_input":"2023-04-19T00:48:37.24483Z","iopub.status.idle":"2023-04-19T00:48:37.251977Z","shell.execute_reply.started":"2023-04-19T00:48:37.244791Z","shell.execute_reply":"2023-04-19T00:48:37.250717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#TODO: Join train_terms_df.EntryID on train_taxonomy_df.EntryID","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n##⚪ Information accretion: IA.txt contains the information accretion (weights) for each GO term. These weights are used to compute weighted precision and recall, as described in the Evaluation section of the competition.
💌 Thank you for taking the time to read through my notebook. I hope you found it interesting and informative. If you have any feedback or suggestions for improvement, please don't hesitate to let me know in the comments.
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