# Predicting effects of noncoding variants with deep learning–based sequence model | 基于深度学习的序列模型预测非编码区变异的影响

Predicting effects of noncoding variants with deep learning–based sequence model

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Interpreting noncoding variants - 非常好的学习资料

This is, to our knowledge, the first approach for prioritization of functional variants using de novo regulatory sequence information.

## 理解数据

List of all publicly available chromatin feature profile files used for training DeepSEA

ENCODE有哪些public的数据？本文中用了哪些数据来训练？

• DHS - wgEncodeAwgDnaseUniform
• TF binding - wgEncodeAwgTfbsUniform
• Histone marks - roadmap

To prepare the input for the deep convolutional network model, we split the genome into 200-bp bins. For each bin we computed the label for all 919 chromatin features; a chromatin feature was labeled 1 if more than half of the 200-bp bin is in the peak region and 0 otherwise.

The 1,000-bp DNA sequence is represented by a 1,000 × 4 binary matrix, with columns corresponding to A, G, C and T. The 400-bp flanking regions at the two sides provide extra contextual information to the model.

For positive standards we used single-nucleotide substitution variants annotated as regulatory mutations in the HGMD professional version 2014.4 (ref. 17),

eQTL data from the GRASP 2.0.0.0 database with a P-value cutoff of 1 × 10−10 (ref. 1)

GWAS SNPs downloaded from the NHGRI GWAS Catalog on 17 July 2014 (ref. 18).

SNP和eQTL的数据是如何被结合到当前的model里的？

To compute predicted chromatin effects of variants using the DeepSEA model, for each SNP, we obtained the 1,000-bp sequence centered on that variant based on the reference genome (specifically, the sequence is chosen so that the variant was located at the 500th nucleotide). Then we constructed a pair of sequences carrying either the reference or alternative allele at the variant position.

For each of the three variant types, HGMD single-nucleotide substitution regulatory mutations, eQTLs and GWAS SNPs, we trained a regularized logistic regression model, using the XGBoost implementation

The HGMD regulatory mutation model was trained with L1 regularization parameter 20 and L2 regularization parameter 2,000 for ten iterations. eQTL and GWAS SNP models were trained with L1 regularization parameter 0 and L2 regularization parameter 10 for 100 iterations.

HGMD：Substitutions causing regulatory abnormalities are logged in with thirty nucleotides flanking the site of the mutation on both sides. The location of the mutation relative to the transcriptional initiation site, initiaton codon, polyadenylation site or termination codon is given.

GRASP: Genome-Wide Repository of Associations Between SNPs and Phenotypes 这里的eQTL应该是每一个SNP高度相关的基因表达的Rank。

## 理解问题

directly learns a regulatory sequence code from large-scale chromatin-profiling data, enabling prediction of chromatin effects of sequence alterations with single-nucleotide sensitivity.

no method has been demonstrated to predict with single-nucleotide sensitivity the effects of noncoding variants on transcription factor (TF) binding, DNA accessibility and histone marks of sequences.

A quantitative model accurately estimating binding of chromatin proteins and histone marks from DNA sequence with singlenucleotide sensitivity is key to this challenge.

Therefore, accurate sequence-based prediction of chromatin features requires a flexible quantitative model capable of modeling such complex dependencies—and those predictions may then be used to estimate functional effects of noncoding variants.基于序列的，可以考虑到更多复杂的情况，这比单纯的依赖现有的motif要更加准确。

We first directly learn regulatory sequence code from genomic sequence by learning to simultaneously predict large-scale chromatin-profiling data, including TF binding, DNase I sensitivity and histone-mark profiles.

sequence context

multilayer

share learned predictive sequence features

model1：TF binding, DNase I sensitivity and histone-mark profiles

FASTA

>[known_CEBP_binding_increase]GtoT__chr1_109817091_109818090
GTGCCTCTGGGAGGAGAGGGACTCCTGGGGGGCCTGCCCCTCATACGCCATCACCAAAAGGAAAGGACAAAGCCACACGC
AGCCAGGGCTTCACACCCTTCAGGCTGCACCCGGGCAGGCCTCAGAACGGTGAGGGGCCAGGGCAAAGGGTGTGCCTCGT
CCTGCCCGCACTGCCTCTCCCAGGAACTGGAAAAGCCCTGTCCGGTGAGGGGGCAGAAGGACTCAGCGCCCCTGGACCCC
CAAATGCTGCATGAACACATTTTCAGGGGAGCCTGTGCCCCCAGGCGGGGGTCGGGCAGCCCCAGCCCCTCTCCTTTTCC
TGGACTCTGGCCGTGCGCGGCAGCCCAGGTGTTTGCTCAGTTGCTGACCCAAAAGTGCTTCATTTTTCGTGCCCGCCCCG
CGCCCCGGGCAGGCCAGTCATGTGTTAAGTTGCGCTTCTTTGCTGTGATGTGGGTGGGGGAGGAAGAGTAAACACAGTGC
TGGCTCGGCTGCCCTGAGGTTGCTCAATCAAGCACAGGTTTCAAGTCTGGGTTCTGGTGTCCACTCACCCACCCCACCCC
CCAAAATCAGACAAATGCTACTTTGTCTAACCTGCTGTGGCCTCTGAGACATGTTCTATTTTTAACCCCTTCTTGGAATT
GGCTCTCTTCTTCAAAGGACCAGGTCCTGTTCCTCTTTCTCCCCGACTCCACCCCAGCTCCCTGTGAAGAGAGAGTTAAT
ATATTTGTTTTATTTATTTGCTTTTTGTGTTGGGATGGGTTCGTGTCCAGTCCCGGGGGTCTGATATGGCCATCACAGGC
TGGGTGTTCCCAGCAGCCCTGGCTTGGGGGCTTGACGCCCTTCCCCTTGCCCCAGGCCATCATCTCCCCACCTCTCCTCC
CCTCTCCTCAGTTTTGCCGACTGCTTTTCATCTGAGTCACCATTTACTCCAAGCATGTATTCCAGACTTGTCACTGACTT
TCCTTCTGGAGCAGGTGGCTAGAAAAAGAGGCTGTGGGCA
>[known_FOXA2_binding_decrease]ReferenceAllele__chr10_23507864_23508863
CTTCTTTTTATCTCTTAACTAACTTACAATTTCTTACGTGATTTTAAAACTTGTTTTTCTATTTAAAACAACAGGGGCAA
CTGAACTTCACTTTCAAACAATATTTATTTCTATAAATCAGTGCAAAACATACTTATTGAAAATATATCTTGGGTCCAAG
GCTTCAAAGGGTAAAAAGAAAGATTTTAAATTATATCTAATATGTTACAATTGTTCTGTCCTTTAAAAACCTTTTCAGAT
CACCCCCTGGATGATTCTTCCCTAGAAGTCTCAGAGAATTAACAACACAATGTAATCTAGGTTTAAATTTGGGTTTCTCC
TGTGTTTCAGATACTGATGTTTGAGCTTTCTCTTCCTGACAAGCCACTTAAAGAGTCACTGTTACTTTGAGGTTTTATCT
GTAAGATTCGTGTCTTTTGGGCTCATTAAGAACATTTCCAAAGATTACAATGTCAATAGCACCTAATTACTGGACTGTGA
GAAAGGTCTTCTTGAGTACATAAAATCTGTGGCAGTGCACAGTACACAATGGGCAGCTCAGATCCCAAATTTTATCACAA
GTAAGTAGCAAACAAATTAATAATGTTACCTGTGCTCTCTTGGATAATTACTACTGCATAAAAACTGCTTTGAAATGTTG
CAGATAGTATTGTACCTCATTTTTTTAATCCCCTTAGAGTAACAAGGATTTATTTGTCTCAAACTTTCTATGTTGCATGC
ACCACTTGACTTTCTTGTTCTGTTTAGAATTTTTAGAACTTGCAACATAACAAAAAATCATTTTTAACCAGCCTAGGAAG
GACATATCACCTGATGTAACATTATTTTAAATTATATTTTGTATTTTACTTTACTCTTTTCAAAACATATACTGTATGTT
TTGATACTATTGCTAGATTTTATTTTTTACTTATGCCTGGTAGAAAATCAGCTATTAAAGAAGCAGAGGAGGCTGGACAC
AGTGGTTCATGTCTGTAATCGCTAGCACTTTGAAAGAGTA
>[known_GATA1_binding_increase]TtoC__chr16_209210_210209
GGGCTTAGACAGAGGAGGGGAGGATTCAGATTTTAAATGGGTTGGCCACTGTAGGTCTATTAACGTGGTGACATTTGAGG
GAGTGGCAATACTAGGGAAGGGGCTTCAGGGGAGTGGCCAGGAGCTAGGGATAGAGGGAGGGAGGACAGGAGGCCTTGTC
TGTCTTTTCCTCCATATGTAAGTTTCAGGAGTGAGTGGGGGGTGTCGAGGGTGCTGTGCTCTCCGGCCTGAGCCTCAGGA
AGGAAGGGCAGTAGTCAGGGATGCCAGGGAAGGACAGTGGAGTAGGCTTTGTGGGGAACTTCACGGTTCCATTGTTGAGA
TGATTTGCTGGAGACACACAGATGAGGACATCAAATACATCCCTGGATCAGGCCCTGGGGCCTGAGTCCGGAAGAGAGGT
CTGTATGGACACACCCATCAATGGGAGCACCAGGACACAGATGGAGGCTAATGTCATGTTGTAGACAGGATGGGTGCTGA
GCTGCCACACCCACATTATCAGAAAATAACAGCACAGGCTTGGGGTGGAGGCGGGACACAAGACTAGCCAGAAGGAGAAA
GAAAGGTGAAAAGCTGTTGGTGCAAGGAAGCTCTTGGTATTTCCAATGGCTTGGGCACAGGCTGTGAGGGTGCCTGGGAC
GGCTTGTGGGGCACAGGCTGCAAGAGGTGCCCAGGACGGCTTGTGGGGCACAGGTTGTGAGAGGTGCCCTGGACGGCTTG
TGGGGCACAGGCTGTGAGAGGTGCCCAGGACGGCTTGTGGGGCACAGGCTGTGAGGGTGCCCGGGACGGCTTGTGGGGCA
CAGGTTGTGAGAGGTGCCCGGGACGGCTTGTGGGGCACAGGTTTCAGAGGTGCCCGGGACGGCTTGTGGGGCACAGGTTG
TGAGAGGTGCCCGGGACGGCTTGTGGGACACAGGTTGTGAGAGGTGCCTGGGACGGCTTGTGGGGCACAGGCTGTGAGGG
TGCCTGGGACGGCTTGTGGGGCACAGGTTGTGAGAGGTGC
>[known_FOXA1_binding_increase]CtoT_chr16_52598689_52599688
GGCTCAAGCAGTCCTCCCATCTAGGCTTCCCAAAATGCTGGGATTACAGACATGAGCCACTGCACCCAGCCACAAAGATA
ACCTAAAGATGTGTTTACTTTGACCCAGGCAGTAGTTTAAAAAAGTTTTAATTTGTTGTTCACATTTAAAAACTGGACAA
TTTCTACATAAAAATCTGAATTACTCATGTCTCTTAAAAAAATAACATCTAGCAATGGTAGGCCCACATTCCTTCCTGAA
AATAATTAGCTGGGAAAGAGTAGGGACTGACCCCTTTAGACACGGTATAAATAGCATGGGAGTTGATCAGTAAATATTTG
CTGAATGAAAGAATACATGAATGAAAAGTCAGAGCCCTATAGGTCAGCATGGACGGCGGTAAAGGAACCTGGCTGAGCCT
GAAAGAGAATGTGATCTAAGATTAAATCCAGGATATGCTGGTAAATGTTTAACAGCCAACTCTTTGGGGAGGAAAAAAGT
CCCAATTTGTAGTGTTTGCTGATTATTGTGATGTAAATACTCCCATCATGACCAATTTCAAGCTACCAACATGCTGACAC
TGAACTTGGAGTTGGAAGGAGATGAACAGGCATAATCAGGTCTCGTGAGATGGCCCAAGCCGGCCCCAGCACTCCACTGT
TATATATGAGGCTAGAATTACTACATAACTGGAATAGCAACTTTCTGGACCATATGCCTGGAACACAGCAGGTGCTGAAT
AAATGTTTGTTGATCCAGGAACTGACTGTGTTGAAGCCCACAGATGGGAAATCAGTAGAAGGCAGGTAAGAGTAAAAAGA
AGGGCAGAGAATTGGGGGTACAGACCCCTGAACCATAAGTCAGAGGAATGTTGTACATGTTTTCAGATCCCTCACTGGTC
AAATGAAGGCAAAGGGTTAGATCTCTCCAAATCTTTAGAGGGACATGATGTAACTCCATTAAGTAACTCAGTGATTTTCA
ACATTAAAAAGTGTAATTATCTTTTCAAACTAAATATTAC　　

VCF

chr1	109817590	[known_CEBP_binding_increase]	G	T
chr10	23508363	[known_FOXA2_binding_decrease]	A	G
chr16	52599188	[known_FOXA1_binding_increase]	C	T
chr16	209709	[known_GATA1_binding_increase]	T	C　　

BED

chr1	109817090	109818090	.	0	*
chr10	23507863	23508863	.	0	*
chr16	52598688	52599688	.	0	*
chr16	209209	210209	.	0	*


ENCODE and Roadmap Epigenomics data were used for labeling and the HG19 human genome was used for input sequences. The data is splitted to training, validation and test sets. The genomic regions are splitted to 200bp bins and labeled according to chromatin profiles. We kept the bins that have at least one TF binding event (note that TF binding event is measured by any overlap with a TF peak, not the >50% overlap criterion used for labeling).

DEPENDENCIES

1. Install CUDA driver. A high-end NVIDIA CUDA compatible graphics card with enough memory is required to train the model. I use Tesla K20m with 5Gb memory for training the model.

2. Installing torch and basic package dependencies following instructions from
http://torch.ch/docs/getting-started.html
You may need to install cmake first if you do not have it installed. It is highly recommended to link against OpenBLAS or other optimized BLAS library when building torch, since it makes a huge difference in performance while running on CPU.

3. Install torch packages required for training only: cutorch, cunn, mattorch. You may install through luarock install [PACKAGE_NAME] command. Note mattorch requires matlab. If you do not have matlab, you may try out https://github.com/soumith/matio-ffi.torch and change 1_data.lua to use matio instead (IMPORTANT: if you use matio, place remove the ":tr\
anspose(3,1)" and "transpose(2,1)" operation in 1_data.lua. The dimesions have been correctly handled by matio.).

Usage Example

th main.lua -save results

The output folder will be under ./results . The folder will inlcude the model file as well as log files for monitoring training progress.

You can specify various parameters for main.lua e.g. set learning rate by -LearningRate. Take a look at main.lua for the options.

Short explanation of the code: 1_data.lua reads the training and validation data; 2_model.lua specify the model; 3_loss.lua specify the loss function; 4_train.lua do the training.

"in silico saturated mutagenesis" analysis for discovering informative sequence features within any sequence.

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posted @ 2019-09-05 00:06  Life·Intelligence  阅读(...)  评论(...编辑  收藏