update readme for additional datasets

README.md

@@ -16,17 +16,23 @@ To achieve this we strike a balance between task complexity and computational co
 
 ## 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>
+allow the user to specify an arbitrarily long sequence length, giving more context 
+to the task, by passing the `sequence_length` kwarg to `load_dataset`. Additional task 
+specific kwargs, if applicable, are mentioned 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}  |     89060  |     8862    |
-
+| Task                                 | `task_name`                            | Sample Output                                                                             | # Train Seqs | # Test Seqs |
+|--------------------------------------|----------------------------------------|-------------------------------------------------------------------------------------------| ------------ | ----------- |
+| Variant Effect Causal eQTL           | `variant_effect_causal_eqtl`           | {ref sequence, alt sequence, label, tissue, chromosome,position, distance to nearest TSS} |     88717  |     8846    |
+| Variant Effect Pathogenic Clinvar    | `variant_effect_pathogenic_clinvar`    | {ref sequence, alt sequence, label, chromosome, position}                                 |     38634  |     1018    |
+| Variant Effect Pathogenic OMIM       | `variant_effect_pathogenic_omim`       | {ref sequence, alt sequence, label,chromosome, position}                                  |     -  |     2321473    |
+| CAGE Prediction                      | `cage_prediction`                      | {sequence, labels, chromosome}                                                            |     33891        |     1922    |
+| Bulk RNA Expression                  | `bulk_rna_expression`                  | {sequence, labels, chromosome,position}                                                   |     22827       |     990     |
+| Chromatin Features Histone_Marks     | `chromatin_features_histone_marks`     | {sequence, labels,chromosome, position}                                                   |     2203689  |     227456    |
+| Chromatin Features DNA_Accessibility | `chromatin_features_dna_accessibility` | {sequence, labels,chromosome, position}                                                   |     2203689  |     227456    |
+| Regulatory Elements Promoter         | `regulatory_element_promoter`          | {sequence, label,chromosome, start, stop}                                                 |     953376  |     96240    |
+| Regulatory Elements Enhancer         | `regulatory_element_enhancer`          | {sequence, label,chromosome, start, stop}                                                 |     1914575  |     192201    |
 
 ## Usage Example
 ```python
@@ -35,8 +41,13 @@ 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"
+# One of:
+# ["variant_effect_causal_eqtl","variant_effect_pathogenic_clinvar",
+# "variant_effect_pathogenic_omim","cage_prediction", "bulk_rna_expression",
+# "chromatin_features_histone_marks","chromatin_features_dna_accessibility",
+# "regulatory_element_promoter","regulatory_element_enhancer"] 
+
+task_name = "variant_effect_causal_eqtl"
 
 dataset = load_dataset(
     "InstaDeepAI/genomics-long-range-benchmark",
@@ -59,7 +70,8 @@ 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. 
+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
@@ -84,11 +96,6 @@ Output: a variable length vector depending on the requested sequence length [req
 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>
-
 
 ---
 
@@ -123,7 +130,7 @@ R<sup>2</sup>
 
 ---
 
-### 3. Variant Effect Gene Expression
+### 3. Variant Effect Causal eQTL
 In genomics, a key objective is to predict how genetic variants affect gene expression in specific cell types.
 
 #### Source
@@ -151,7 +158,124 @@ Output: a binary value refering to whether the variant has an effect on gene exp
 Train: chromosomes 1-8, 11-22, X, Y<br>
 Test: chromosomes 9,10
 
-#### Metrics
-Accuracy<br>
-AUROC<br>
-AUPRC
+---
+
+### 4. Variant Effect Pathogenic ClinVar
+This task differs from the above in that variants in this datasets are linked to disease pathogenicity rather than effect on gene expression. Specifically, this tasks involves
+predicting whether a variant located in a coding region is pathogenic or is a common missense variant.
+
+#### Source
+Original data comes from ClinVar and gnomAD. However, we use processed data files from the [GPN-MSA paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592768/) located here :https://huggingface.co/datasets/songlab/human_variants/blob/main/test.parquet .
+Sequence data originates from the GRCh38 genome assembly.
+
+#### Data Processing
+Human labelers generated the pathogenic missense set from ClinVar. The non-pathogenic set comes from common missense variants in gnomAD.
+
+#### 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<br>
+Output: a binary value referring to whether the variant is pathogenic or not
+
+#### Splits
+Train: chromosomes 1-7, 9-22, X, Y<br>
+Test: chromosomes 8
+
+---
+
+### 5. Variant Effect Pathogenic OMIM
+This tasks involves predicting whether a variant located in a regulatory region is 
+implicated in a mendelian disease or is a common missense variant.
+
+#### Source
+Original data comes from the Online Mendelian Inheritance in Man (OMIM) and gnomAD 
+databases. 
+However, we use processed data files from the 
+[GPN-MSA paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10592768/) located here :https://huggingface.co/datasets/songlab/omim/blob/main/test.parquet.
+Sequence data originates from the GRCh38 genome assembly.
+
+#### Data Processing
+Pathogenic variants originate from OMIM. The non-pathogenic set comes 
+from common missense variants in gnomAD.
+
+#### Task Structure
+
+Type: Binary classification<br>
+
+Task Args:<br>
+`sequence_length`: an interger type, the desired final sequence length<br>
+`subset`: a boolean type, whether to use the full dataset or a subset of the dataset (we provide this option as the full dataset has millions of samples)
+
+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<br>
+Output: a binary value referring to whether the variant is pathogenic or not
+
+#### Splits
+Test: all chromosomes
+
+---
+### 6. Chromatin Features 
+Chromatin refers to the mixture of DNA and proteins that form the chromosomes and allow the DNA to exist in a compact structure. There are many factors describing the state of
+chromatin which play important roles in regulating the cell. In this task we present two features which represent chromatin state, histone marks and DNA accessibility. Histones are proteins which in combination with 
+a short strand of DNA form the basic unit of chromatin called the nucleosome. Histone marks correspond to the chemical modifications of histones which include methylation, acetylation,
+phosphorylation, and ubiquitination. DNA accessibility as measured through DNase I–hypersensitive sites refers to regions of the chromosome where DNA is not bound in a nucleosome but 
+rather is open and accessible for other proteins such as transcription factors to bind.
+
+#### Source
+Original data used to generate labels for histone marks and DNase profiles comes from the ENCODE and Roadmap Epigenomics project. We used processed data files from the [Deep Sea paper](https://www.nature.com/articles/nmeth.3547) to build this dataset.
+Sequence data originates from the GRCh37/hg19 genome assembly.
+
+#### Data Processing
+As we use the data from Deep Sea the labels were generated according to the their following procedure - the genome was split into 200 base pairs bins and bins were labeled
+ as 1 if more than half of the bin overlapped with the peak region of the chromatin feature annotations and 0 otherwise.
+
+Data from Deep Sea contains 919 labels for various chromatin features - 690 TF binding profiles, 125 DNase profiles and 104 histone-mark profiles. To make the dataset more accessible 
+while still providing a representative set, we randomly sampled 20 histone marks and 20 DNase tissues.
+
+#### Task Structure
+
+Type: Multi-label binary classification
+
+Task Args:<br>
+`sequence_length`: an interger type, the desired final sequence length<br>
+`subset`: a boolean type, whether to use the full dataset or a subset of the dataset (we provide this option as the full dataset has millions of samples)
+
+Input: a genomic nucleotide sequence centered on the 200 base pair bin that is associated with the labels<br>
+Output: a vector of length 20 with binary entries
+
+#### Splits
+Train set: chromosomes 1-7,10-22<br>
+Test set: chromosomes 8,9
+
+---
+### 7. Regulatory Element 
+Regulatory elements are segments of DNA that play a pivotal role in controlling gene expression and cellular processes. 
+These elements include promoters, enhancers, and other regions that interact with transcription factors and other proteins 
+to modulate the activity of genes. Identifying regulatory elements is essential for understanding the complex regulatory 
+networks governing cellular functions. 
+
+#### Source
+Original data annotations to build labels came from the Search Candidate cis-Regulatory Elements by ENCODE project. Sequence data originates from the GRCh38 
+genome assembly.
+
+#### Data Processing
+To generate labels from the annotations we first split the genome into 200 base pairs bins. We label a bin as 1 if more than half of the bin overlaps with any regulatory
+element given in the annotations and 0 otherwise.
+
+#### Task Structure
+
+Type: Binary classification
+
+Task Args:<br>
+`sequence_length`: an interger type, the desired final sequence length<br>
+`subset`: a boolean type, whether to use the full dataset or a subset of the dataset (we provide this option as the full dataset has millions of samples)
+
+Input: a genomic nucleotide sequence centered on the 200 base pair bin that is associated with the label<br>
+Output: a single binary value
+
+#### Splits
+Train set: chromosomes 1-7,10-22<br>
+Test set: chromosomes 8,9