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README.md

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 ---
 license: cc-by-nc-sa-4.0
+language:
+- en
+tags:
+- biology
+- genomics
+pretty_name: Genomics Long Range Benchmark
 ---
+
+## Summary
+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. 
+While serving as a strong basis of evaluation, the benchmark must also be efficient and user-friendly. 
+To achieve this we strike a balance between task complexity and computational cost through strategic decisions, such as down-sampling or combining datasets.
+
+## Dataset Tasks
+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 
+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,
+ are mentioend in the sections below.<br>
+*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 
+cause indexing outside the boundaries of chromosomes.
+
+|        Task         |        `task_name`        |      Sample Output          | # Train Seqs | # Test Seqs |
+|        ---------         |         ----------        |      ------          | ------------ | ----------- |
+| CAGE Prediction | `cage_prediction`|  {sequence, labels, chromosome}  |     36086        |     1922    |
+| Bulk RNA Expression |   `bulk_rna_expression`    |  {sequence, labels, chromosome}  |     22827       |     990     |
+| Variant Effect Gene Expression |   `variant_effect_gene_expression`    |  {ref sequence, alt sequence, label, tissue, chromosome, distance to nearest TSS}  |     88717  |     8846    |
+
+
+### 1. CAGE Prediction
+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 
+degradation. Being able to accurately predict mRNA levels as measured by CAGE is essential for deciphering tissue-specific expression patterns, transcriptional networks, and 
+identifying differentially expressed genes with functional significance. 
+
+#### Source
+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).
+Sequence data originates from the GRCh38 genome assembly.
+
+#### Data Processing
+The original dataset from the Basenji paper includes labels for 638 CAGE total tracks over 896 bins (each bin corresponding to 128 base pairs) 
+totaling over ~70 GB. In the interest of dataset size and user friendliness, only a subset of the labels are selected. 
+From the 638 CAGE tracks, 50 of these tracks are selected with the following criteria:
+
+  1. Only select one cell line
+  2. Only keep mock treated and remove other treatments
+  3. Only select one donor
+     
+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`.
+
+#### Task Structure
+
+Type: Multi-variable regression<br>
+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
+you request a sequence length smaller than 114,688 bps than the labels will be subsetted.
+
+Task Args:<br>
+`sequence_length`: an interger type, the desired final sequence length, *must be a multiple of 128 given the binned nature of labels<br>
+
+Input: a genomic nucleotide sequence centered around the labeled region of the gene transcription start site<br>
+Output: a variable length vector depending on the requested sequence length [requested_sequence_length / 128, 50]
+
+#### Splits
+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 
+set with the train set and use cross validation to select a new train and validation set from this combined set.
+
+#### Metrics
+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>
+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>
+R<sup>2</sup>
+
+
+---
+
+### 2. Bulk RNA Expression
+In comparison to CAGE, bulk RNA sequencing assays measure the steady state level (both transription and degradation) of mRNA in a population of cells.
+
+#### Source
+Original data comes from GTEx. We use processed data files from the [ExPecto paper](https://www.nature.com/articles/s41588-018-0160-6) found 
+[here](https://github.com/FunctionLab/ExPecto/tree/master/resources). Sequence data originates from the GRCh37/hg19 genome assembly.
+
+#### Data Processing
+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`.
+
+#### Task Structure
+
+Type: Multi-variable regression<br>
+
+Task Args:<br>
+`sequence_length`: an interger type, the desired final sequence length<br>
+
+Input: a genomic nucleotide sequence centered around the CAGE representative trancription start site<br>
+Output: a 218 length vector of continuous values corresponding to the bulk RNA expression levels in 218 different tissue types
+
+#### Splits
+Train: chromosomes 1-7,9-22,X,Y<br>
+Test: chromosome 8
+
+#### Metrics
+Mean Spearman correlation across tissues <br>
+Mean Spearman correlation across genes <br>
+R<sup>2</sup>
+
+---
+
+### 3. Variant Effect Gene Expression
+In genomics, a key objective is to predict how genetic variants affect gene expression in specific cell types.
+
+#### Source
+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). 
+Sequence data originates from the GRCh38 genome assembly.
+
+#### Data Processing
+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 
+and provide the tissue type along with each sample.
+
+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 
+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.
+
+#### Task Structure
+
+Type: Binary classification<br>
+
+Task Args:<br>
+`sequence_length`: an interger type, the desired final sequence length<br>
+
+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>
+Output: a binary value refering to whether the variant has an effect on gene expression 
+
+#### Splits
+Train: chromosomes 1-8, 11-22, X, Y<br>
+Test: chromosomes 9,10
+
+#### Metrics
+Accuracy<br>
+AUROC
+AUPRC