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TideHunter

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TideHunter: efficient and sensitive tandem repeat detection from noisy long reads using seed-and-chain

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# TideHunter: efficient and sensitive tandem repeat detection from noisy long reads using seed-and-chain
[![Latest Release](https://img.shields.io/github/release/yangao07/TideHunter.svg?label=Release)](https://github.com/yangao07/TideHunter/releases/latest)
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[![BioConda Install](https://img.shields.io/conda/dn/bioconda/tidehunter.svg?style=flag&label=BioConda%20install)](https://anaconda.org/bioconda/tidehunter)
[![Published in Bioinformatics](https://img.shields.io/badge/Published%20in-Bioinformatics-blue.svg)](https://doi.org/10.1093/bioinformatics/btz376)
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## Updates (v1.5.6)
* Fix 1-bp shift/missing in consensus/unit sequence

## Getting started
Download the [latest release](https://github.com/yangao07/TideHunter/releases):
```
wget https://github.com/yangao07/TideHunter/releases/download/v1.5.6/TideHunter-v1.5.6.tar.gz
tar -zxvf TideHunter-v1.5.6.tar.gz && cd TideHunter-v1.5.6
```
Make from source and run with test data:
```
make; ./bin/TideHunter ./test_data/test_50x4.fa > cons.fa
```
Or, install via conda and run with test data:
```
conda install -c bioconda tidehunter
TideHunter ./test_data/test_50x4.fa > cons.fa
```
## Table of Contents

- [TideHunter: efficient and sensitive tandem repeat detection from noisy long reads using seed-and-chain](#tidehunter-efficient-and-sensitive-tandem-repeat-detection-from-noisy-long-reads-using-seed-and-chain)
  - [Updates (v1.5.6)](#updates-v156)
  - [Getting started](#getting-started)
  - [Table of Contents](#table-of-contents)
  - [Introduction](#introduction)
  - [Installation](#installation)
    - [Installing TideHunter via conda](#installing-tidehunter-via-conda)
    - [Pre-built binary executable file for Linux/Mac OS](#pre-built-binary-executable-file-for-linuxmac-os)
    - [Building TideHunter from source files](#building-tidehunter-from-source-files)
  - [Getting started with toy example in `test_data`](#getting-started-with-toy-example-in-test_data)
  - [Usage](#usage)
      - [To generate consensus sequences in FASTA format](#to-generate-consensus-sequences-in-fasta-format)
      - [To generate consensus sequences in tabular format](#to-generate-consensus-sequences-in-tabular-format)
      - [To generate consensus sequences in FASTQ format](#to-generate-consensus-sequences-in-fastq-format)
      - [To generate full-length consensus sequences](#to-generate-full-length-consensus-sequences)
      - [To generate unit sequences in FASTA format](#to-generate-unit-sequences-in-fasta-format)
      - [To generate unit sequences in tabular format](#to-generate-unit-sequences-in-tabular-format)
  - [Input](#input)
    - [Adapter sequence](#adapter-sequence)
  - [Output](#output)
    - [Tabular format](#tabular-format)
    - [FASTA format](#fasta-format)
    - [FASTQ format](#fastq-format)
    - [Unit sequences](#unit-sequences)
  - [Contact](#contact)

## <a name="introduction"></a>Introduction
TideHunter is an efficient and sensitive tandem repeat detection and
consensus calling tool which is designed for tandemly repeated
long-read sequence ([INC-seq](https://doi.org/10.1186/s13742-016-0140-7),
 [R2C2](https://doi.org/10.1073/pnas.1806447115), [NanoAmpli-Seq](https://doi.org/10.1093/gigascience/giy140)).

It works with Pacific Biosciences (PacBio) and 
Oxford Nanopore Technologies (ONT) sequencing data at error rates 
up to 20% and does not have any limitation of the maximal repeat pattern size.

## <a name="install"></a>Installation

### <a name="conda"></a>Installing TideHunter via conda
On Linux/Unix and Mac OS, TideHunter can be installed via
```
conda install -c bioconda tidehunter
```

### <a name="binary"></a>Pre-built binary executable file for Linux/Mac OS
Linux x86_64:
```
wget https://github.com/yangao07/TideHunter/releases/download/v1.5.6/TideHunter-v1.5.6_x64-linux.tar.gz
tar -zxvf TideHunter-v1.5.6_x64-linux.tar.gz
```
Mac OS arm64:
```
wget https://github.com/yangao07/TideHunter/releases/download/v1.5.6/TideHunter-v1.5.6_arm64-macos.tar.gz
tar -zxvf TideHunter-v1.5.6_arm64-macos.tar.gz
```

### <a name="build"></a>Building TideHunter from source files
You can also build TideHunter from source files.
Make sure you have gcc (>=6.4.0) and zlib installed before compiling.
It is recommended to download the latest release of TideHunter 
from the [release page](https://github.com/yangao07/TideHunter/releases).
```
wget https://github.com/yangao07/TideHunter/releases/download/v1.5.6/TideHunter-v1.5.6.tar.gz
tar -zxvf TideHunter-v1.5.6.tar.gz
cd TideHunter-v1.5.6; make
```

## <a name="start"></a>Getting started with toy example in `test_data`
```
TideHunter ./test_data/test_1000x10.fa > cons.fa
```

## <a name="usage"></a>Usage
#### <a name="fasta_cons"></a>To generate consensus sequences in FASTA format
```
TideHunter ./test_data/test_1000x10.fa > cons.fa
```
#### <a name="tab_cons"></a>To generate consensus sequences in tabular format
```
TideHunter -f 2 ./test_data/test_1000x10.fa > cons.out
```
#### <a name="fq_cons"></a>To generate consensus sequences in FASTQ format
```
TideHunter -f 3 ./test_data/test_1000x10.fa > cons.fq
```
#### <a name="full_cons"></a>To generate full-length consensus sequences
```
TideHunter -5 ./test_data/5prime.fa -3 ./test_data/3prime.fa ./test_data/full_length.fa > cons_full.fa
```
#### <a name="fasta_unit"></a>To generate unit sequences in FASTA format
```
TideHunter -u ./test_data/test_1000x10.fa > unit.fa
```
#### <a name="tab_unit"></a>To generate unit sequences in tabular format
```
TideHunter -u -f 2 ./test_data/test_1000x10.fa > unit.out
```

## <a name="input_output"></a>Input
TideHunter works with FASTA, FASTQ, gzip'd FASTA(.fa.gz) and gzip'd FASTQ(.fq.gz) formats.

### <a name="adapter"></a>Adapter sequence
Additional adapter sequence files can be provided to TideHunter with `-5` and `-3` options.

TideHunter uses adapter information to search for the full-length sequence from the generated consensus.

Once two adapters are found, TideHunter trims and reorients the consensus sequence.

## <a name="output"></a>Output
TideHunter can output consensus sequence in FASTA format by default, 
it can also provide output in tabular format.

### <a name="tabular"></a>Tabular format
For tabular format, 9 columns will be generated for each consensus sequence:

| No. | Column name | Explanation | 
|:---:|   :---      | ---        |
|  1  | readName    | the original read name |
|  2  | repN        | `N` is the ID number of the tandem repeat, within each read, starts from 0 |
|  3  | copyNum     | copy number of the tandem repeat |
|  4  | readLen     | length of the original long read |
|  5  | start       | start coordinate of the tandem repeat, 1-based |
|  6  | end         | end coordinate of the tandem repeat, 1-based |
|  7  | consLen     | length of the consensus sequence |
|  8  | aveMatch    | average percent of matches between each unit sequence and the consensus sequence (# matched bases / unit length)|
|  9  | fullLen     | 0: not a full-length sequence, 1: sense strand full-length, 2: anti-sense strand full-length |
|  10  | subPos     | start coordinates of all the tandem repeat unit sequence, followed by the end coordinate of the last tandem repeat unit sequence, separated by `,`, all coordinates are 1-based, see examples below|
| 11  | consSeq     | consensus sequence |

For example, here are the output for a non-full-length consensus sequence generated from [test_data/test_50x4.fa](test_data/test_50x4.fa) and the adiagram that illustrates all the coordiantes in the output:
```
test_50x4 rep0 4.0 300 51 250 50 100.0 0 59,109,159,208 CGATCGATCGGCATGCATGCATGCTAGTCGATGCATCGGGATCAGCTAGT
```

<img src="img/example_50x4.png" width="800">

In this example, TideHunter identifies three consecutive tandem repeat units, [59,108], [109,158], [159,208], from the raw read which is 300 bp long.
A consensus sequence with 50 bp is generated from the three repeat units. TideHunter further extends the tandem repeat boundary to [51, 250] by aligning the consensus sequence back to the raw read on both sides of the three repeat units.

Another example of the output for a full-length consensus sequence generated from [test_data/full_length.fa](test_data/full_length.fa):
```
8f2f7766-4b8e-4c0d-9e2b-caf0e5527b19  rep0  8.8  5231  31  5215  203 95.7  1 207,798,1386,1976,2563,3155,3746,4333,4930  ACTAATAAGATCAACAGAATCAGAGTAGATAGTTCCTTGATCGGAACCAAAGGACCCCGTGCCTCAATCTCTATCCTGATGTCATGGGAGTCCTAGCAAAGCTATAGACTCAAGCAAGGCTTGGGGTCCTTTATGGAACCCAAGGATGACTCAGCAATAAAATATTTTGGTTTTGGTTTATAAAAAAAAAAAAAAAAAAAAAA
```
In this example, the `consLen` (i.e., 203) is the length of the full-length consensus sequence excluding the 5' and 3' adapter sequences and the `subPos` (i.e., 207,798,1386,1976,2563,3155,3746,4333,4930) contains the coordinate information of the identified tandem repeat units.

### <a name="fasta"></a>FASTA format
For FASTA output format, the read name and the comment provide detailed information of the detected tandem repeat, 
i.e., the above columns 1 \~ 10.
The sequence is the consensus sequence.

The read name and comment of each consensus sequence have the following format:
```
>readName_repN_copyNum readLen_start_end_consLen_aveMatch_fullLen_subPos
```

### <a name="fastq"></a>FASTQ format
For FASTQ output format, the read name and comment are the same as described in [FASTA format](#fasta).
TideHunter calculated a customized Phred score as the base quality score of each consensus base:

Here, <img src="https://latex.codecogs.com/svg.image?p"/> is the Sigmoid-smoothed consensus calling error rate for each base:

<img src="https://latex.codecogs.com/svg.image?S"/> is the Sigmoid function:

 <img src="img/sigmoid.svg"/>

<img src="https://latex.codecogs.com/svg.image?N_{cons}"/> is the coverage of the consensus base and
<img src="https://latex.codecogs.com/svg.image?N_{total}"/> is the number of total copies. 
For example, if one base of the consensus sequence has 4 supporting copies and the total copy number is 5,
<img src="https://latex.codecogs.com/svg.image?N_{cons}"/> is 4 and <img src="https://latex.codecogs.com/svg.image?N_{total}"/> is 5.

The Phred quality score was then shifted by 33 and converted to characters based on the ASCII value.
The quality scores range from 0 to 60 and the corresponding ASCII values range from 33 to 93.

### <a name="unit"></a>Unit sequences
TideHunter can output the unit sequences without performing the consensus calling step when option `-u/--unit-seq` is enabled. Then, only the following information will be output for the tabular format:

| No. | Column name | Explanation | 
|:---:|   :---      | ---        |
|  1  | readName    | the original read name |
|  2  | repN        | `N` is the ID number of the tandem repeat, within each read, starts from 0 |
|  3  | subX        | `X` is the ID number of the unit sequence, starts from 0 |
|  4  | unitSeq     | unit sequence |

And for the FASTA format:
```
>readName_repN_subX
unitSeq X
>readName_repN_subY
unitSeq Y
```

## <a name="contact"></a>Contact
Yan Gao gaoy1@chop.edu

Yi Xing XINGYI@chop.edu

[github issues](https://github.com/yangao07/TideHunter/issues)

TideHunter

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