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--- |
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license: cc-by-nc-sa-4.0 |
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language: |
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- en |
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tags: |
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- biology |
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- genomics |
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pretty_name: Genomics Long Range Benchmark |
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## Summary |
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The motivation of the genomics long range benchmark (LRB) is to compile a set of biologically relevant genomic tasks requiring long-range dependencies which will act as a robust evaluation tool for genomic language models. |
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While serving as a strong basis of evaluation, the benchmark must also be efficient and user-friendly. |
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To achieve this we strike a balance between task complexity and computational cost through strategic decisions, such as down-sampling or combining datasets. |
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## Dataset Tasks |
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The Genomics LRB is a collection of tasks which can be loaded by passing in the corresponding `task_name` into the `load_dataset` function. All of the following datasets |
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allow the user to specify an arbitrarily long sequence length, giving more context to the task, by passing `sequence_length` kwarg to `load_dataset`. Additional task specific kwargs, if applicable, |
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are mentioend in the sections below.<br> |
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*Note that as you increase the context length to very large numbers you may start to reduce the size of the dataset since a large context size may |
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cause indexing outside the boundaries of chromosomes. |
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| Task | `task_name` | Sample Output | # Train Seqs | # Test Seqs | |
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| --------- | ---------- | ------ | ------------ | ----------- | |
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| CAGE Prediction | `cage_prediction`| {sequence, labels, chromosome} | 36086 | 1922 | |
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| Bulk RNA Expression | `bulk_rna_expression` | {sequence, labels, chromosome} | 22827 | 990 | |
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| Variant Effect Gene Expression | `variant_effect_gene_expression` | {ref sequence, alt sequence, label, tissue, chromosome, distance to nearest TSS} | 89060 | 8862 | |
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### 1. CAGE Prediction |
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Cap Analysis Gene Expression(CAGE) is a biological assay used to measure the level of mRNA production rather than steady state values, taking into account both production and |
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degradation. Being able to accurately predict mRNA levels as measured by CAGE is essential for deciphering tissue-specific expression patterns, transcriptional networks, and |
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identifying differentially expressed genes with functional significance. |
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#### Source |
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Original CAGE data comes from FANTOM5. We used processed labeled data obtained from the [Basenji paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5932613/) which also used to train Enformer and is located [here](https://console.cloud.google.com/storage/browser/basenji_barnyard/data/human?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))&prefix=&forceOnObjectsSortingFiltering=false). |
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Sequence data originates from the GRCh38 genome assembly. |
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#### Data Processing |
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The original dataset from the Basenji paper includes labels for 638 CAGE total tracks over 896 bins (each bin corresponding to 128 base pairs) |
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totaling over ~70 GB. In the interest of dataset size and user friendliness, only a subset of the labels are selected. |
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From the 638 CAGE tracks, 50 of these tracks are selected with the following criteria: |
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1. Only select one cell line |
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2. Only keep mock treated and remove other treatments |
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3. Only select one donor |
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The [896 bins, 50 tracks] labels total in at ~7 GB. A description of the 50 included CAGE tracks can be found here `cage_prediction/label_mapping.csv`. |
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#### Task Structure |
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Type: Multi-variable regression<br> |
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Because this task involves predicting expression levels for 128bp bins and there are 896 total bins in the dataset, there are in essence labels for 896 * 128 = 114,688 basepair sequences. If |
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you request a sequence length smaller than 114,688 bps than the labels will be subsetted. |
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Task Args:<br> |
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`sequence_length`: an interger type, the desired final sequence length, *must be a multiple of 128 given the binned nature of labels<br> |
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Input: a genomic nucleotide sequence centered around the labeled region of the gene transcription start site<br> |
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Output: a variable length vector depending on the requested sequence length [requested_sequence_length / 128, 50] |
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#### Splits |
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Train/Test splits were maintained from Basenji and Enformer where randomly sampling was used to generate the splits. Note that for this dataset a validation set is also returned. In practice we merged the validation |
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set with the train set and use cross validation to select a new train and validation set from this combined set. |
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#### Metrics |
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Mean Pearson correlation across tracks - compute Pearson correlation for a track using all positions for all genes in the test set, then mean over all tracks <br> |
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Mean Pearson correlation across genes - compute Pearson correlation for a gene using all positions and all tracks, then mean over all genes in the test set <br> |
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R<sup>2</sup> |
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--- |
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### 2. Bulk RNA Expression |
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In comparison to CAGE, bulk RNA sequencing assays measure the steady state level (both transription and degradation) of mRNA in a population of cells. |
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#### Source |
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Original data comes from GTEx. We use processed data files from the [ExPecto paper](https://www.nature.com/articles/s41588-018-0160-6) found |
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[here](https://github.com/FunctionLab/ExPecto/tree/master/resources). Sequence data originates from the GRCh37/hg19 genome assembly. |
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#### Data Processing |
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The continuous labels were log(1+x) transformed and standardized. A list of names of tissues corresponding to the labels can be found here: `bulk_rna_expression/label_mapping.csv`. |
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#### Task Structure |
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Type: Multi-variable regression<br> |
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Task Args:<br> |
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`sequence_length`: an interger type, the desired final sequence length<br> |
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Input: a genomic nucleotide sequence centered around the CAGE representative trancription start site<br> |
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Output: a 218 length vector of continuous values corresponding to the bulk RNA expression levels in 218 different tissue types |
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#### Splits |
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Train: chromosomes 1-7,9-22,X,Y<br> |
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Test: chromosome 8 |
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#### Metrics |
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Mean Spearman correlation across tissues <br> |
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Mean Spearman correlation across genes <br> |
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R<sup>2</sup> |
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--- |
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### 3. Variant Effect Gene Expression |
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In genomics, a key objective is to predict how genetic variants affect gene expression in specific cell types. |
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#### Source |
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Original data comes from GTEx. However, we used processed data files from the [Enformer paper](https://www.nature.com/articles/s41592-021-01252-x) located [here](https://console.cloud.google.com/storage/browser/dm-enformer/data/gtex_fine/vcf?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))&prefix=&forceOnObjectsSortingFiltering=false). |
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Sequence data originates from the GRCh38 genome assembly. |
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#### Data Processing |
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In Enformer the datasets were partitioned in 48 different sets based on the tissue types. In our framing of the task we combine all samples across all tissues into one set |
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and provide the tissue type along with each sample. |
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As data files were used from Enformer, the labels were constructed according to their methodology - variants were labeled as 1 if their posterior inclusion probability was greater than 0.9 as |
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assigned by the population-based fine-mapping tool SuSiE, while a matched set of negative variants was built with posterior inclusion probabilities of less than .01. |
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#### Task Structure |
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Type: Binary classification<br> |
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Task Args:<br> |
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`sequence_length`: an interger type, the desired final sequence length<br> |
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Input: a genomic nucleotide sequence centered on the SNP with the reference allele at the SNP location, a genomic nucleotide sequence centered on the SNP with the alternative allele at the SNP location, and tissue type<br> |
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Output: a binary value refering to whether the variant has an effect on gene expression |
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#### Splits |
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Train: chromosomes 1-8, 11-22, X, Y<br> |
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Test: chromosomes 9,10 |
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#### Metrics |
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Accuracy<br> |
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AUROC<br> |
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AUPRC |
