ipyrad command line tutorial - Part I

This is the first part of the full tutorial for the command line interface (CLI) for ipyrad. In this tutorial we’ll walk through the entire assembly and analysis process. This is meant as a broad introduction to familiarize users with the general workflow, and some of the parameters and terminology. We will use as an example in this tutorial the Anolis data set from the first part of class. However, you can follow along with one of the other example data sets if you like and although your results will vary the procedure will be identical.

If you are new to RADseq analyses, this tutorial will provide a simple overview of how to execute ipyrad, what the data files look like, how to check that your analysis is working, and what the final output formats will be. We will also cover how to run ipyrad on a cluster and to do so efficiently.

Each grey cell in this tutorial indicates a command line interaction. Lines starting with $ indicate a command that should be executed in a terminal connected to the Habanero cluster, for example by copying and pasting the text into your terminal. Elements in code cells surrounded by angle brackets (e.g. ) are variables that need to be replaced by the user. All lines in code cells beginning with \#\# are comments and should not be copied and executed. All other lines should be interpreted as output from the issued commands.

## Example Code Cell.
## Create an empty file in my home directory called `watdo.txt`
$ touch ~/watdo.txt

## Print "wat" to the screen
$ echo "wat"

Overview of Assembly Steps

Very roughly speaking, ipyrad exists to transform raw data coming off the sequencing instrument into output files that you can use for downstream analysis.


The basic steps of this process are as follows:

Note on files in the project directory: Assembling rad-seq type sequence data requires a lot of different steps, and these steps generate a lot of intermediary files. ipyrad organizes these files into directories, and it prepends the name of your assembly to each directory with data that belongs to it. One result of this is that you can have multiple assemblies of the same raw data with different parameter settings and you don’t have to manage all the files yourself! (See Branching assemblies for more info). Another result is that you should not rename or move any of the directories inside your project directory, unless you know what you’re doing or you don’t mind if your assembly breaks.

Getting Started

If you haven’t already installed ipyrad go here first: installation

Working with the cluster


A typical high performance computing (HPC) cluster architecture looks somewhat like this, just with many many more “compute nodes”. The “head node” (or login node) is normally the only system that you will interact with. If you want to run a big job on the cluster, you will connect to the head node (with ssh), and will submit a job to the ‘work queue’. In this job you specify how many cores you want to run on, how much RAM you want, and how much time you think it’ll take. Then the work queue looks at your job and at all the other jobs in the queue and figures out when is the fairest time to start your job running. This can be almost immediately, or your job might sit in the queue for hours or days, if the system is very busy. Either way, you will rarely actually “see” your job run because you normally aren’t given access to the compute nodes directly.

This is usually okay, because usually if you want to run a “big” job, this means you want to run tons of small, quick tasks, or one or a few really really huges and slow tasks, and you kind of don’t care what’s happening, so long as they finish at some point. For us, for the benefit of exposing and monitoring the processes for the tutorial, having jobs locked away in the work queue is inconvenient. Fortunately, many HPC systems provide an “interactive” mode, which allows you to run certain limited tasks inside a terminal directly on one of the compute nodes.

Therefore, we will run most of this tutorial on assembly and analysis on the Habanero cluster inside an “interactive” job. This will allow us to run our proccesses on compute nodes, but still be able to remain at the command line so we can easily monitor the progress. If you do not still have an active ssh window on the cluster, begin by re-establishing the connection through puTTY (Windows) or ssh (Mac/Linux):

$ ssh <username>@habanero.rcs.columbia.edu

Submitting an interactive job to the cluster

Now we will submit an interactive job with relatively modest resource requests. Every cluster has different limits on its resources in terms of what is available to you and for how long. We could find these limits for the Columbia Habanero cluster by googling it. In this case we will each request that 4 cores be made available to us for 1 hour. Because each node has 24 cores this means that multiple people will actually be sharing the same node, which is not a problem.

# --pty tells it to connect us to compute nodes interactively
# --account tells it which account's resources to use
# --reservation tells it to use the resources on edu reserved for us
# -t tells it how much time to connect for
# /bin/bash tells it to open a bash terminal when we connect.
$ srun --pty --account=edu --reservation=edu_23 -t 1:00:00 -c 4 /bin/bash

Depending on cluster usage the job submission script can take more or less time to start. Because we have these resources reserved for us there should be very little wait time. Once your job starts your terminal will show that you are now connected to a compute node.

Inspecting running cluster processes

At any time you can ask the cluster for the status of your jobs with the squeue command. This will list every running job on the cluster which can be a pain to sort through. So for efficiency you can add the argument -u <username> to limit it to showing just your jobs.

## Check the status of my running job on the 'proto' Queue
$ squeue -u work1
 8367625      edu2     bash    work1  R       0:09      1 node215

This confirms that the job we submitted started, that it’s running on node215, etc.

ipyrad help

To better understand how to use ipyrad, let’s take a look at the help argument. We will use some of the ipyrad arguments in this tutorial (for example: -n, -p, -s, -c, -r). But, the complete list of optional arguments and their explanation is below.

$ ipyrad --help
usage: ipyrad [-h] [-v] [-r] [-f] [-q] [-d] [-n new] [-p params]
              [-b [branch [branch ...]]] [-m [merge [merge ...]]] [-s steps]
              [-c cores] [-t threading] [--MPI] [--preview]
              [--ipcluster [ipcluster]] [--download [download [download ...]]]

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  -r, --results         show results summary for Assembly in params.txt and
  -f, --force           force overwrite of existing data
  -q, --quiet           do not print to stderror or stdout.
  -d, --debug           print lots more info to ipyrad_log.txt.
  -n new                create new file 'params-{new}.txt' in current
  -p params             path to params file for Assembly:
  -b [branch [branch ...]]
                        create a new branch of the Assembly as
  -m [merge [merge ...]]
                        merge all assemblies provided into a new assembly
  -s steps              Set of assembly steps to perform, e.g., -s 123
  -c cores              number of CPU cores to use (Default=0=All)
  -t threading          tune threading of binaries (Default=2)
  --MPI                 connect to parallel CPUs across multiple nodes
  --preview             run ipyrad in preview mode. Subset the input file so
                        it'll runquickly so you can verify everything is
  --ipcluster [ipcluster]
                        connect to ipcluster profile (default: 'default')
  --download [download [download ...]]
                        download fastq files by accession (e.g., SRP or SRR)

  * Example command-line usage: 
    ipyrad -n data                       ## create new file called params-data.txt 
    ipyrad -p params-data.txt            ## run ipyrad with settings in params file
    ipyrad -p params-data.txt -s 123     ## run only steps 1-3 of assembly.
    ipyrad -p params-data.txt -s 3 -f    ## run step 3, overwrite existing data.

  * HPC parallelization across 32 cores
    ipyrad -p params-data.txt -s 3 -c 32 --MPI

  * Print results summary 
    ipyrad -p params-data.txt -r 

  * Branch/Merging Assemblies
    ipyrad -p params-data.txt -b newdata  
    ipyrad -m newdata params-1.txt params-2.txt [params-3.txt, ...]

  * Subsample taxa during branching
    ipyrad -p params-data.txt -b newdata taxaKeepList.txt

  * Download sequence data from SRA into directory 'sra-fastqs/' 
    ipyrad --download SRP021469 sra-fastqs/ 

  * Documentation: http://ipyrad.readthedocs.io

Create a new parameters file

ipyrad uses a text file to hold all the parameters for a given assembly. Start by creating a new parameters file with the -n flag. This flag requires you to pass in a name for your assembly. In the example we use anolis but the name can be anything at all. Once you start analysing your own data you might call your parameters file something more informative, like the name of your organism and some details on the settings.

# go to our working directory
$ cd ~/ipyrad-workshop

# create a new params file named 'anolis' (Or the name of an alternative library)
$ ipyrad -n anolis

This will create a file in the current directory called params-anolis.txt. The params file lists on each line one parameter followed by a ## mark, then the name of the parameter, and then a short description of its purpose. Lets take a look at it.

$ cat params-anolis.txt
------- ipyrad params file (v.0.7.28)-------------------------------------------
anolis                         ## [0] [assembly_name]: Assembly name. Used to name output directories for assembly steps
./                             ## [1] [project_dir]: Project dir (made in curdir if not present)
                               ## [2] [raw_fastq_path]: Location of raw non-demultiplexed fastq files
                               ## [3] [barcodes_path]: Location of barcodes file
                               ## [4] [sorted_fastq_path]: Location of demultiplexed/sorted fastq files
denovo                         ## [5] [assembly_method]: Assembly method (denovo, reference, denovo+reference, denovo-reference)
                               ## [6] [reference_sequence]: Location of reference sequence file
rad                            ## [7] [datatype]: Datatype (see docs): rad, gbs, ddrad, etc.
TGCAG,                         ## [8] [restriction_overhang]: Restriction overhang (cut1,) or (cut1, cut2)
5                              ## [9] [max_low_qual_bases]: Max low quality base calls (Q<20) in a read
33                             ## [10] [phred_Qscore_offset]: phred Q score offset (33 is default and very standard)
6                              ## [11] [mindepth_statistical]: Min depth for statistical base calling
6                              ## [12] [mindepth_majrule]: Min depth for majority-rule base calling
10000                          ## [13] [maxdepth]: Max cluster depth within samples
0.85                           ## [14] [clust_threshold]: Clustering threshold for de novo assembly
0                              ## [15] [max_barcode_mismatch]: Max number of allowable mismatches in barcodes
0                              ## [16] [filter_adapters]: Filter for adapters/primers (1 or 2=stricter)
35                             ## [17] [filter_min_trim_len]: Min length of reads after adapter trim
2                              ## [18] [max_alleles_consens]: Max alleles per site in consensus sequences
5, 5                           ## [19] [max_Ns_consens]: Max N's (uncalled bases) in consensus (R1, R2)
8, 8                           ## [20] [max_Hs_consens]: Max Hs (heterozygotes) in consensus (R1, R2)
4                              ## [21] [min_samples_locus]: Min # samples per locus for output
20, 20                         ## [22] [max_SNPs_locus]: Max # SNPs per locus (R1, R2)
8, 8                           ## [23] [max_Indels_locus]: Max # of indels per locus (R1, R2)
0.5                            ## [24] [max_shared_Hs_locus]: Max # heterozygous sites per locus (R1, R2)
0, 0, 0, 0                     ## [25] [trim_reads]: Trim raw read edges (R1>, <R1, R2>, <R2) (see docs)
0, 0, 0, 0                     ## [26] [trim_loci]: Trim locus edges (see docs) (R1>, <R1, R2>, <R2)
p, s, v                        ## [27] [output_formats]: Output formats (see docs)
                               ## [28] [pop_assign_file]: Path to population assignment file

In general the defaults are sensible, and we won’t mess with them for now, but there are a few parameters we must change: the path to the raw data, the dataype, and the restriction overhang sequence.

We will use the nano text editor to modify params-anolis.txt and change these parameters:

$ nano params-anolis.txt


Nano is a command line editor, so you’ll need to use only the arrow keys on the keyboard for navigating around the file. Nano accepts a few special keyboard commands for doing things other than modifying text, and it lists these on the bottom of the frame.

We need to specify where the raw data files are located, the type of data we are using (.e.g., ‘gbs’, ‘rad’, ‘ddrad’, ‘pairddrad), and which enzyme cut site overhangs are expected to be present on the reads. We list the parameter settings for three different empirical libraries below. Choose just one for your example analysis.

# Anolis data set
anoles                         ## [2] project_dir
/rigel/edu/radcamp/files/anoles/*.gz                    ## [4] [sorted_fastq_path]: Location of demultiplexed/sorted fastq files
gbs                            ## [7] [datatype]: Datatype (see docs): rad, gbs, ddrad, etc.
TGCAT,                         ## [8] [restriction_overhang]: Restriction overhang (cut1,) or (cut1, cut2)
# Pedicularis data set
pedicularis                    ## [2] project_dir
/rigel/edu/radcamp/files/SRP021469/*.gz                    ## [4] [sorted_fastq_path]: Location of demultiplexed/sorted fastq files
rad                            ## [7] [datatype]: Datatype (see docs): rad, gbs, ddrad, etc.
TGCAG,                         ## [8] [restriction_overhang]: Restriction overhang (cut1,) or (cut1, cut2)
# Finch data set
finch                          ## [2] project_dir
/rigel/edu/radcamp/files/SRP059199/*.gz                    ## [4] [sorted_fastq_path]: Location of demultiplexed/sorted fastq files
ddrad                          ## [7] [datatype]: Datatype (see docs): rad, gbs, ddrad, etc.
CCTGCAGG,AATTC                 ## [8] [restriction_overhang]: Restriction overhang (cut1,) or (cut1, cut2)

After you change these parameters you may save and exit nano by typing CTRL+o (to write Output), and then CTRL+x (to eXit the program).

Note: The CTRL+x notation indicates that you should hold down the control key (which is often styled ‘ctrl’ on the keyboard) and then push ‘x’.

Once we start running the analysis ipyrad will create several new directories to hold the output of each step for this assembly. By default the new directories are created in the project_dir directory and use the prefix specified by the assembly_name parameter. Because we use ./ for the project_dir for this tutorial, all these intermediate directories will be of the form: ~/ipyrad-workshop/anolis_*, or the analagous name that you used for your assembly name.

Note: Again, the ./ notation indicates the current working directory. You can always view the current working directory with the pwd command (print working directory).

Input data format

Before we get started let’s take a look at what the raw data looks like.

Your input data will be in fastQ format, usually ending in .fq, .fastq, .fq.gz, or .fastq.gz. The file/s may be compressed with gzip so that they have a .gz ending, but they do not need to be. When loading pre-demultiplexed data (as we are with the Anolis data) the location of raw sample files should be entered on line 3 of the params file. Below are the first three reads of one of the Anolis files.

## For your personal edification here is what this is doing:
## gunzip -c: Tells gzip to unzip the file and write the contents to the screen
## head -n 12: Grabs the first 12 lines of the fastq file. Fastq files
## have 4 lines per read, so the value of `-n` should be a multiple of 4

$ zcat /rigel/edu/radcamp/files/SRP021469/29154_superba_SRR1754715.fastq.gz | head -n 20
@D00656:123:C6P86ANXX:8:2201:3857:34366 1:Y:0:8
@D00656:123:C6P86ANXX:8:2201:5076:34300 1:N:0:8
@D00656:123:C6P86ANXX:8:2201:5042:34398 1:N:0:8

Each read is composed of four lines. The first is the name of the read (its location on the plate). The second line contains the sequence data. The third line is unused. And the fourth line is the quality scores for the base calls. The FASTQ wikipedia page has a good figure depicting the logic behind how quality scores are encoded.

The Anolis data are 96bp single-end reads prepared as GBS. The first five bases (TGCAT) form the restriction site overhang. All following bases make up the sequence data.

Step 1: Loading the raw data files

With reads already demultiplexed to samples, step 1 simply scans through the raw data, verifies the input format, and counts reads per sample. It doesn’t create any new directories or modify the raw files in any way.

Note on step 1: More commonly, rather than returning demultiplexed samples as we have here, sequencing facilities will give you one giant .gz file that contains all the sequences from your run. This situation only slightly modifies step 1, and does not modify further steps, so we will refer you to the full ipyrad tutorial for guidance in this case.

Now lets run step 1! For the Anolis data this will take <1 minute.

Special Note: In interactive mode please be aware to always specify the number of cores with the -c flag. If you do not specify the number of cores ipyrad assumes you want all of them, but in this case you only have as many cores available as we requested when we started the interactive session. This can cause some confusion that will slow things down a bit. So specify the number of cores that you know are available in this case when using interactive mode.

## -p    the params file we wish to use
## -s    the step to run
## -c    the number of cores to allocate   <-- Important!
$ ipyrad -p params-anolis.txt -s 1 -c 4

  ipyrad [v.0.7.28]
  Interactive assembly and analysis of RAD-seq data
  New Assembly: anolis
  establishing parallel connection:
  host compute node: [4 cores] on darwin

  Step 1: Loading sorted fastq data to Samples
  [####################] 100%  loading reads         | 0:00:04  
  10 fastq files loaded to 10 Samples.

In-depth operations of running an ipyrad step

Any time ipyrad is invoked it performs a few housekeeping operations:

  1. Load the assembly object - Since this is our first time running any steps we need to initialize our assembly.
  2. Start the parallel cluster - ipyrad uses a parallelization library called ipyparallel. Every time we start a step we fire up the parallel clients. This makes your assemblies go smokin’ fast.
  3. Do the work - Actually perform the work of the requested step(s) (in this case loading in sample reads).
  4. Save, clean up, and exit - Save the state of the assembly, and spin down the ipyparallel cluster.

As a convenience ipyrad internally tracks the state of all your steps in your current assembly, so at any time you can ask for results by invoking the -r flag. We also use the -p arg to tell is which params file (i.e., which assembly) we want it to print stats for.

## -r fetches informative results from currently executed steps  
$ ipyrad -p params-anolis.txt -r
Summary stats of Assembly anolis
                   state  reads_raw
punc_IBSPCRIB0361      1     250000
punc_ICST764           1     250000
punc_JFT773            1     250000
punc_MTR05978          1     250000
punc_MTR17744          1     250000
punc_MTR21545          1     250000
punc_MTR34414          1     250000
punc_MTRX1468          1     250000
punc_MTRX1478          1     250000
punc_MUFAL9635         1     250000

Full stats files
step 1: ./anolis_s1_demultiplex_stats.txt
step 2: None
step 3: None
step 4: None
step 5: None
step 6: None
step 7: None

If you want to get even more info ipyrad tracks all kinds of wacky stats and saves them to a file inside the directories it creates for each step. For instance to see full stats for step 1 (the wackyness of the step 1 stats at this point isn’t very interesting, but we’ll see stats for later steps are more verbose):

$ cat anolis_s1_demultiplex_stats.txt 
punc_IBSPCRIB0361     250000
punc_ICST764          250000
punc_JFT773           250000
punc_MTR05978         250000
punc_MTR17744         250000
punc_MTR21545         250000
punc_MTR34414         250000
punc_MTRX1468         250000
punc_MTRX1478         250000
punc_MUFAL9635        250000

Step 2: Filter reads

This step filters reads based on quality scores and maximum number of uncalled bases, and can be used to detect Illumina adapters in your reads, which is sometimes a problem under couple different library prep scenarios. Recalling from our exploration of the data with FastQC we have some problem with adapters, and a little noise toward the 3’ end. To account for this we will trim reads to 75bp and set adapter filtering to be quite aggressive.

Note: Trimming to 75bp seems a bit aggressive too, and based on the FastQC results you probably would not want to do this with if these were your real data. However, it will speed up the analysis considerably. Here, we are just trimming the reads for the sake of this workshop.

Edit your params file again with nano:

nano params-anolis.txt

and change the following two parameter settings:

2                               ## [16] [filter_adapters]: Filter for adapters/primers (1 or 2=stricter)
0, 75, 0, 0                     ## [25] [trim_reads]: Trim raw read edges (R1>, <R1, R2>, <R2) (see docs)

Note: Saving and quitting from nano: CTRL+o then CTRL+x

$ ipyrad -p params-anolis.txt -s 2 -c 4
  ipyrad [v.0.7.28]
  Interactive assembly and analysis of RAD-seq data
  loading Assembly: anolis
  from saved path: ~/ipyrad-workshop/anolis.json
  establishing parallel connection:
  host compute node: [4 cores] on darwin

  Step 2: Filtering reads 
  [####################] 100%  processing reads      | 0:01:02

The filtered files are written to a new directory called anolis_edits. Again, you can look at the results output by this step and also some handy stats tracked for this assembly.

## View the output of step 2
$ cat anolis_edits/s2_rawedit_stats.txt 
                   reads_raw  trim_adapter_bp_read1  trim_quality_bp_read1  reads_filtered_by_Ns  reads_filtered_by_minlen  reads_passed_filter
punc_IBSPCRIB0361     250000                 108761                 160210                    66                     12415               237519
punc_ICST764          250000                 107320                 178463                    68                     13117               236815
punc_JFT773           250000                 110684                 190803                    46                      9852               240102
punc_MTR05978         250000                 102932                 144773                    54                     12242               237704
punc_MTR17744         250000                 103394                 211363                    55                      9549               240396
punc_MTR21545         250000                 119191                 161709                    63                     21972               227965
punc_MTR34414         250000                 109207                 193401                    54                     16372               233574
punc_MTRX1468         250000                 119746                 134069                    45                     19052               230903
punc_MTRX1478         250000                 116009                 184189                    53                     16549               233398
punc_MUFAL9635        250000                 114492                 182877                    61                     18071               231868
## Get current stats including # raw reads and # reads after filtering.
$ ipyrad -p params-anolis.txt -r

You might also take a closer look at the filtered reads:

$ zcat anolis_edits/punc_IBSPCRIB0361.trimmed_R1_.fastq.gz | head -n 12
@D00656:123:C6P86ANXX:8:2201:3857:34366 1:Y:0:8
@D00656:123:C6P86ANXX:8:2201:5076:34300 1:N:0:8
@D00656:123:C6P86ANXX:8:2201:5042:34398 1:N:0:8

This is actually really cool, because we can already see the results of both of our applied parameters. All reads have been trimmed to 75bp, and the third read had adapter contamination removed (you can tell because it’s shorter than 75bp). As an exercise you can go back up to the section where we looked at the raw data initially and see if you can identify the adapter sequence in this read. We will see more about the consequences of filtering adapters soon as well when we look at the clustered reads next.

Step 3: clustering within-samples

Step 3 de-replicates and then clusters reads within each sample by the set clustering threshold and then writes the clusters to new files in a directory called anolis_clust_0.85. Intuitively we are trying to identify all the reads that map to the same locus within each sample. The clustering threshold specifies the minimum percentage of sequence similarity below which we will consider two reads to have come from different loci.

The true name of this output directory will be dictated by the value you set for the clust_threshold parameter in the params file.

You can see the default value is 0.85, so our default directory is named accordingly. This value dictates the percentage of sequence similarity that reads must have in order to be considered reads at the same locus. You’ll more than likely want to experiment with this value, but 0.85 is a reliable default, balancing over-splitting of loci vs over-lumping. Don’t mess with this until you feel comfortable with the overall workflow, and also until you’ve learned about Branching assemblies.

There have been many papers written comparing how results of assemblies vary depending on the clustering threshold. In general, my advice is to use a value between about .82 and .95. Within this region results typically do not vary too significantly, whereas above .95 you will oversplit loci and recover fewer SNPs.

It’s also possible to incorporate information from a reference genome to improve clustering at this step, if such a resources is available for your organism (or one that is relatively closely related). We will not cover reference based assemblies in this workshop, but you can refer to the ipyrad documentation for more information.

Note on performance: Steps 3 and 6 generally take considerably longer than any of the steps, due to the resource intensive clustering and alignment phases. These can take on the order of 10-100x as long as the next longest running step. This depends heavily on the number of samples in your dataset, the number of cores, the length(s) of your reads, and the “messiness” of your data in terms of the number of unique loci present (this can vary from a few thousand to many millions).

Now lets run step 3:

$ ipyrad -p params-anolis.txt -s 3 -c 2
  ipyrad [v.0.7.28]
  Interactive assembly and analysis of RAD-seq data
  loading Assembly: anolis
  from saved path: ~/ipyrad-workshop/anolis.json
  establishing parallel connection:
  host compute node: [2 cores] on darwin

  Step 3: Clustering/Mapping reads
  [####################] 100%  dereplicating         | 0:00:11  
  [####################] 100%  clustering            | 0:19:35
  [####################] 100%  building clusters     | 0:00:06
  [####################] 100%  chunking              | 0:00:01
  [####################] 100%  aligning              | 0:14:27
  [####################] 100%  concatenating         | 0:00:04```

In-depth operations of step 3:

Again we can examine the results. The stats output tells you how many clusters were found (‘clusters_total’), and the number of clusters that pass the mindepth thresholds (‘clusters_hidepth’). We’ll go into more detail about mindepth settings in some of the advanced tutorials but for now all you need to know is that by default step 3 will filter out clusters that only have a handful of reads on the assumption that these are probably all mostly due to sequencing error.

$ ipyrad -p params-anolis.txt -r
Summary stats of Assembly anolis
                   state  reads_raw  reads_passed_filter  clusters_total  clusters_hidepth
punc_IBSPCRIB0361      3     250000               237519           56312              4223
punc_ICST764           3     250000               236815           60626              4302
punc_JFT773            3     250000               240102           61304              5214
punc_MTR05978          3     250000               237704           61615              4709
punc_MTR17744          3     250000               240396           62422              5170
punc_MTR21545          3     250000               227965           55845              3614
punc_MTR34414          3     250000               233574           61242              4278
punc_MTRX1468          3     250000               230903           54411              3988
punc_MTRX1478          3     250000               233398           57299              4155
punc_MUFAL9635         3     250000               231868           59249              3866

Again, the final output of step 3 is dereplicated, clustered files for each sample in ./anolis_clust_0.85/. You can get a feel for what this looks like by examining a portion of one of the files.

We’ll take a moment now to compare the outputs of the different empirical libraries.

## Same as above, `zcat` unzips and prints to the screen and 
## `head -n 28` means just show me the first 28 lines. 
$ zcat anolis_clust_0.85/punc_IBSPCRIB0361.clustS.gz | head -n 28

Reads that are sufficiently similar (based on the above sequence similarity threshold) are grouped together in clusters separated by “//”. For the second and fourth clusters above these are probably homozygous with some sequencing error, but it’s hard to tell. For the first and third clusters, are there truly two alleles (heterozygote)? Is it a homozygote with lots of sequencing errors, or a heterozygote with few reads for one of the alleles?

Thankfully, untangling this mess is what step 4 is all about.