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.
*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}	89060	8862

Usage Example

from datasets import load_dataset

# Use this parameter to download sequences of arbitrary length (see docs below for edge cases)
sequence_length=2048

# One of ["cage_prediction", "bulk_rna_expression", "variant_effect_gene_expression"] 
task_name = "variant_effect_gene_expression"

dataset = load_dataset(
    "InstaDeepAI/genomics-long-range-benchmark",
    task_name=task_name,
    sequence_length=sequence_length,
)

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 which also used to train Enformer and is located here. 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:

Only select one cell line
Only keep mock treated and remove other treatments
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
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:
sequence_length: an interger type, the desired final sequence length, *must be a multiple of 128 given the binned nature of labels

Input: a genomic nucleotide sequence
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
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
R²

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 found here. 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

Task Args:
sequence_length: an interger type, the desired final sequence length

Input: a genomic nucleotide sequence centered around the CAGE representative trancription start site
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
Test: chromosome 8

Metrics

Mean Spearman correlation across tissues
Mean Spearman correlation across genes
R²

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 located here. 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

Task Args:
sequence_length: an interger type, the desired final sequence length

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
Output: a binary value refering to whether the variant has an effect on gene expression

Splits

Train: chromosomes 1-8, 11-22, X, Y
Test: chromosomes 9,10

Metrics

Accuracy
AUROC
AUPRC