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pauvre

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Pauvre: QC and genome browser plotting Oxford Nanopore and PacBio long reads.

// repository documentation

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## <a name="started"></a>Getting Started

```
pauvre custommargin -i custom.tsv --ycol length --xcol qual # Custom tsv input
```

## Table of Contents

- [Getting Started](#started)
- [Users' Guide](#uguide)
- [Installation](#installation)
  - [Requirements](#reqs)
  - [Install Instructions](#install)
- [Usage](#usage)
  - [pauvre stats](#stats)
  - [pauvre marginplot](#marginplot)
    - [Basic Usage](#marginbasic)
    - [Plot Adjustments](#marginadjustments)
    - [Specialized Options](#marginspecialized)
- [Contributors](#contributors)

## <a name="uguide"></a>Users' Guide

Pauvre is a plotting package originally designed to help QC the length and
quality distribution of Oxford Nanopore or PacBio reads. The main outputs
are marginplots.  Now, `pauvre` also hosts other additional data plotting
scripts.

This package currently hosts five scripts for plotting and/or printing stats.

- `pauvre marginplot`
  - takes a fastq file as input and outputs a marginal histogram with a heatmap.
- `pauvre custommargin`
  - takes a tsv as input and outputs a marginal histogram with custom columns of your choice.
- `pauvre stats`
  - Takes a fastq file as input and prints out a table of stats, including how many basepairs/reads there are for a length/mean quality cutoff.
  - This is also automagically called when using `pauvre marginplot`
- `pauvre redwood`
  - I am happy to introduce the redwood plot to the world as a method
    of representing circular genomes. A redwood plot contains long
    reads as "rings" on the inside, a gene annotation
    "cambrium/phloem", and a RNAseq "bark". The input is `.bam` files
    for the long reads and RNAseq data, and a `.gff` file for the
    annotation. More details to follow as we document this program
    better...
- `pauvre synteny`
  - Makes a synteny plot of circular genomes. Finds the most
    parsimonius rotation to display the synteny of all the input
    genomes with the fewest crossings-over. Input is one `.gff` file
    per circular genome and one directory of gene alignments.

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

### <a name="reqs"></a>Requirements

- You must have the following installed on your system to install this software:
  - python 3.x
  - matplotlib
  - biopython
  - pandas
  - pillow

### <a name="install">Install Instructions
- Instructions to install on your mac or linux system. Not sure on
  Windows! Make sure *python 3* is the active environment before
  installing.
  - `git clone https://github.com/conchoecia/pauvre.git`
  - `cd ./pauvre`
  - `pip3 install .`
- Or, install with pip
  - `pip3 install pauvre`

## <a name="usage"><a/>Usage

### <a name="stats"></a>`stats`
  - generate basic statistics about the fastq file. For example, if I
    want to know the number of bases and reads with AT LEAST a PHRED
    score of 5 and AT LEAST a read length of 500, run the program as below
    and look at the cells highlighted with `<braces>`.
    - `pauvre stats --fastq miniDSMN15.fastq`

```
numReads: 1000
numBasepairs: 1029114
meanLen: 1029.114
medianLen: 875.5
minLen: 11
maxLen: 5337
N50: 1278
L50: 296

Basepairs >= bin by mean PHRED and length
minLen       Q0       Q5     Q10     Q15   Q17.5    Q20  Q21.5   Q25  Q25.5  Q30
     0  1029114  1010681  935366  429279  143948  25139   3668  2938   2000    0
   500   984212  <968653> 904787  421307  142003  24417   3668  2938   2000    0
  1000   659842   649319  616788  300948  103122  17251   2000  2000   2000    0
 et cetera...
              Number of reads >= bin by mean Phred+Len
minLen    Q0   Q5  Q10  Q15  Q17.5  Q20  Q21.5  Q25  Q25.5  Q30
     0  1000  969  865  366    118   22      3    2      1    0
   500   873 <859> 789  347    113   20      3    2      1    0
  1000   424  418  396  187     62   11      1    1      1    0
 et cetera...
```

###  <a name="marginplot"></a>`marginplot`

#### <a name="marginbasic"></a>Basic Usage
- automatically calls `pauvre stats` for each fastq file
- Make the default plot showing the 99th percentile of longest reads
  - `pauvre marginplot --fastq miniDSMN15.fastq`
  - ![default](https://raw.githubusercontent.com/conchoecia/pauvre/master/files/default_miniDSMN15.png)
- Make a marginal histogram for ONT 2D or 1D^2 cDNA data with a
  lower maxlen and higher maxqual.
  - `pauvre marginplot --maxlen 4000 --maxqual 25 --lengthbin 50 --fileform pdf png --qualbin 0.5 --fastq miniDSMN15.fastq`
  - ![example1](https://raw.githubusercontent.com/conchoecia/pauvre/master/files/miniDSMN15.png)

#### <a name="marginadjustments"></a>Plot Adjustments

- Filter out reads with a mean quality less than 5, and a length
  less than 800. Zoom in to plot only mean quality of at least 4 and
  read length at least 500bp.
  - `pauvre marginplot -f miniDSMN15.fastq --filt_minqual 5 --filt_minlen 800 -y --plot_minlen 500 --plot_minqual 4`
  - ![test4](https://raw.githubusercontent.com/conchoecia/pauvre/master/files/test4.png)

#### <a name="marginspecialized"></a>Specialized Options

- Plot ONT 1D data with a large tail
  - `pauvre marginplot --maxlen 100000 --maxqual 15 --lengthbin 500  <myfile>.fastq`
- Get more resolution on lengths
  - `pauvre marginplot --maxlen 100000 --lengthbin 5  <myfile>.fastq`

- Turn off transparency if you just want a white background
  - `pauvre marginplot --transparent False <myfile>.fastq`
  - Note: transparency is the default behavior
    - ![transparency](https://raw.githubusercontent.com/conchoecia/pauvre/master/files/transparency.001.jpeg)

## <a name="contributors"></a>Contributors

@conchoecia (Darrin Schultz)
@mebbert (Mark Ebbert)
@wdecoster (Wouter De Coster)

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