https://github.com/dereneaton/ipyrad
Tip revision: 178c7d84f3d1949cd5118bfc69b627d8a550123c authored by dereneaton on 17 March 2016, 15:23:52 UTC
"Updating ipyrad/__init__.py to version - 0.1.79
"Updating ipyrad/__init__.py to version - 0.1.79
Tip revision: 178c7d8
tutorial_intro_cli.rst
.. include:: global.rst
.. _tutorial_intro_cli:
Introductory tutorial - CLI
============================
This is the full tutorial for the command line interface 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. For simplicity we'll use
single-end RAD-Seq as the example data, but the core concepts will apply
to assembly of other data types (GBS and paired-end).
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. You can
follow along by copy/pasting the code-blocks into a command line terminal.
Getting Started
~~~~~~~~~~~~~~~
.. note::
If you haven't already installed ipyrad go here first:
:ref:`Installation <installation>`
We provide a small sample data set to be used for this tutorial.
Full data sets usually take several hours to several days to complete,
whereas this simulated data set can completed in just a few minutes.
Getting the data
~~~~~~~~~~~~~~~~~
First download and extract a set of example data from the web using the command
below. This will create a directory called ``ipsimdata/`` in your current directory
containing a number of test data sets.
.. code-block:: bash
## The curl command needs a capital O, not a zero
curl -LkO https://github.com/dereneaton/ipyrad/raw/master/tests/ipsimdata.tar.gz
tar -xvzf ipsimdata.tar.gz
Use the command ls to look inside this directory, like below. You'll see that
it contains many different files which are represent different test datasets
for learning about different types of analyses with ipyrad.
.. code-block:: bash
## the command ls shows you the files inside a directory (folder)
ls ipsimdata/
For this introductory tutorial we will use just the following two files from
this directory, which are similar to the types of files you are likely to have
when you begin an analysis. Use a text editor, or the unix command 'less' to
look at each file to see what it contains.
- ``sim_rad_test_R1_.fastq.gz`` - Illumina fastQ formatted reads (gzip compressed)
- ``sim_rad_test_barcodes.txt`` - Mapping of barcodes to sample IDs
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
``iptest`` 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. We will refer to this as
the "assembly_name".
.. code-block:: bash
ipyrad -n iptest
This will create a file in the current directory called ``params-iptest.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 by using the unix command 'cat' (or you can
use any text editor you like).
.. code-block:: bash
cat params-iptest.txt
.. parsed-literal::
------ ipyrad params file (v.0.1.72)--------------------------------------------
iptest ## [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 (only alternative=64)
6 ## [11] [mindepth_statistical]: Min depth for statistical base calling
6 ## [12] [mindepth_majrule]: Min depth for majority-rule base calling
1000 ## [13] [maxdepth]: Max cluster depth within samples
0.85 ## [14] [clust_threshold]: Clustering threshold for de novo assembly
1 ## [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
100, 100 ## [22] [max_SNPs_locus]: Max # SNPs per locus (R1, R2)
5, 99 ## [23] [max_Indels_locus]: Max # of indels per locus (R1, R2)
0.25 ## [24] [max_shared_Hs_locus]: Max # heterozygous sites per locus (R1, R2)
0, 0 ## [25] [edit_cutsites]: Edit cut-sites (R1, R2) (see docs)
1, 2, 2, 1 ## [26] [trim_overhang]: Trim overhang (see docs) (R1>, <R1, R2>, <R2)
* ## [27] [output_formats]: Output formats (see docs)
## [28] [pop_assign_file]: Path to population assignment file
## [29] [excludes]: Samples to be excluded from final output files
## [30] [outgroups]: Outgroup individuals. Excluded from final output files
In general the default parameter values are sensible, and we won't
mess with them for now, but there are a few parameters we *must* change.
We need to set the path to the raw data we
want to analyse, and we need to set the path to the barcodes file.
In your favorite text editor open ``params-iptest.txt`` and change these two lines
to look like this, and then save it:
.. parsed-literal::
./ipsimdata/sim_rad_test_R1_.fastq.gz ## [2] [raw_fastq_path]: Location of raw non-demultiplexed fastq files
./ipsimdata/sim_rad_test_barcodes.txt ## [3] [barcodes_path]: Location of barcodes file
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``. Your data could be split among multiple files, or all
within a single file. The file/s may be compressed with gzip so
that they have a .gz ending, but they do not need to be. The location of
these files should be entered on line 2 of the params file. Below are
the first three reads in the example file.
.. code-block:: bash
## For your personal edification here is what this is doing:
## gzip -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.
gunzip -c ./ipsimdata/sim_rad_test_R1_.fastq.gz | head -n 12
And here's the output:
.. parsed-literal::
@lane1_fakedata0_R1_0 1:N:0:
TTTTAATGCAGTGAGTGGCCATGCAATATATATTTACGGGCGCATAGAGACCCTCAAGACTGCCAACCGGGTGAATCACTATTTGCTTAG
+
BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB
@lane1_fakedata0_R1_1 1:N:0:
TTTTAATGCAGTGAGTGGCCATGCAATATATATTTACGGGCGCATAGAGACCCTCAAGACTGCCAACCGGGTGAATCACTATTTGCTTAG
+
BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB
@lane1_fakedata0_R1_2 1:N:0:
TTTTAATGCAGTGAGTGGCCATGCAATATATATTTACGGGCGCATAGAGACCCTCAAGACTGCCAACCGGGTGAATCACTATTTGCTTAG
+
BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB
Each read takes 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 a spacer. And the fourth line the quality scores
for the base calls. In this case arbitrarily high since the data
were simulated.
These are 100 bp single-end reads prepared as RADseq. The first
six bases form the barcode and the next five bases (TGCAG) the
restriction site overhang. All following bases make up the sequence
data.
Step 1: Demultiplex the raw data files
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Step 1 reads in the barcodes file and the raw data. It scans through
the raw data and sorts each read based on the mapping of samples to
barcodes. At the end of this step we'll have a new directory in our project_dir
called ``iptest_fastqs/``. Inside this directory will be individual
fastq.gz files for each sample.
**NB:** You'll notice the name of this output directory bears a strong
resemblence to the name of the assembly we chose at the time
of the params file creation. 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 :ref:`Branching assemblies <advanced_CLI>` 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.
Lets take a look at the barcodes file for the simulated data. You'll
see sample names (left) and their barcodes (right) each on a
separate line with a tab between them.
.. code-block:: bash
cat ./data/sim_rad_test_barcodes.txt
.. parsed-literal::
1A_0 CATCAT
1B_0 AGTGAT
1C_0 ATGGTA
1D_0 GTGGGA
2E_0 AGGGAA
2F_0 AAAGTG
2G_0 GATATA
2H_0 GAGGAG
3I_0 GGGATT
3J_0 TAATTA
3K_0 TGAGGG
3L_0 ATATTA
Now lets run step 1! For the simulated data this will take < 1 minute.
.. code-block:: bash
## -p indicates the params file we wish to use
## -s indicates the step to run (in this case 1)
ipyrad -p params-iptest.txt -s 1
.. parsed-literal::
--------------------------------------------------
ipyrad [v.0.1.72]
Interactive assembly and analysis of RADseq data
--------------------------------------------------
New Assembly: iptest
ipyparallel setup: Local connection to 4 Engines
Step1: Demultiplexing fastq data to Samples.
Saving Assembly.
There are 4 main parts to this step:
- Create a new assembly. Since this is our first time running any
steps we need to initialize our assembly.
- 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.
- Actually do the demuliplexing.
- Save the state of the assembly.
Have a look at the results of this step in the ``iptest_fastqs/``
output directory:
.. code-block:: bash
ls iptest_fastqs
.. parsed-literal::
1A_0_R1_.fastq.gz 1D_0_R1_.fastq.gz 2G_0_R1_.fastq.gz 3J_0_R1_.fastq.gz s1_demultiplex_stats.txt
1B_0_R1_.fastq.gz 2E_0_R1_.fastq.gz 2H_0_R1_.fastq.gz 3K_0_R1_.fastq.gz
1C_0_R1_.fastq.gz 2F_0_R1_.fastq.gz 3I_0_R1_.fastq.gz 3L_0_R1_.fastq.gz
A more informative metric of success might be the number
of raw reads demultiplexed for each sample. Fortunately
ipyrad 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.
.. code-block:: bash
## -r fetches informative results from currently
## executed steps
ipyrad -p params-iptest.txt -r
.. parsed-literal::
Summary stats of Assembly iptest
------------------------------------------------
state reads_raw
1A_0 1 20099
1B_0 1 19977
1C_0 1 20114
1D_0 1 19895
2E_0 1 19928
2F_0 1 19934
2G_0 1 20026
2H_0 1 19936
3I_0 1 20084
3J_0 1 20011
3K_0 1 20117
3L_0 1 19901
Full stats files
------------------------------------------------
step 1: ./iptest_fastqs/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:
.. code-block:: bash
cat ./iptest_fastqs/s1_demultiplex_stats.txt
And you'll see a ton of fun stuff I won't copy here in the interest
of conserving space. Please go look for yourself if you're interested.
Step 2: Filter reads
~~~~~~~~~~~~~~~~~~~~
This step filters reads based on quality scores, and can be used to
detect Illumina adapters in your reads, which is sometimes a problem
with homebrew type library preparations. Here the filter is set to the
default value of 0 (zero), meaning it filters only based on quality scores of
base calls. The filtered files are written to a new directory called
``iptest_edits/``.
.. code-block:: bash
ipyrad -p params-iptest.txt -s 2
.. parsed-literal::
--------------------------------------------------
ipyrad [v.0.1.72]
Interactive assembly and analysis of RADseq data
--------------------------------------------------
loading Assembly: iptest [~/Documents/ipyrad/tests/iptest.json]
ipyparallel setup: Local connection to 4 Engines
Step2: Filtering reads
Saving Assembly.
Again, you can look at the results output by this step and also some
handy stats tracked for this assembly.
.. code-block:: bash
## View the output of step 2
ls iptest_edits/
.. parsed-literal::
1A_0_R1_.fastq 1C_0_R1_.fastq 2E_0_R1_.fastq 2G_0_R1_.fastq 3I_0_R1_.fastq 3K_0_R1_.fastq s2_rawedit_stats.txt
1B_0_R1_.fastq 1D_0_R1_.fastq 2F_0_R1_.fastq 2H_0_R1_.fastq 3J_0_R1_.fastq 3L_0_R1_.fastq
.. code-block:: bash
## Get current stats including # raw reads and # reads
## after filtering.
ipyrad -p params-iptest.txt -r
.. parsed-literal::
Summary stats of Assembly iptest
------------------------------------------------
state reads_raw reads_filtered
1A_0 2 20099 20099
1B_0 2 19977 19977
1C_0 2 20114 20114
1D_0 2 19895 19895
2E_0 2 19928 19928
2F_0 2 19934 19934
2G_0 2 20026 20026
2H_0 2 19936 19936
3I_0 2 20084 20084
3J_0 2 20011 20011
3K_0 2 20117 20117
3L_0 2 19901 19901
Full stats files
------------------------------------------------
step 1: ./iptest_fastqs/s1_demultiplex_stats.txt
step 2: ./iptest_edits/s2_rawedit_stats.txt
step 3: None
step 4: None
step 5: None
step 6: None
step 7: None
You might also take a gander at the filtered reads:
.. parsed-literal::
head -n 12 ./iptest_edits/1A_0_R1_.fastq
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 ``iptest_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.
.. parsed-literal::
0.85 ## [14] [clust_threshold]: proportion identical for clustering
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 :ref:`Branching assemblies <tutorial_advanced_cli>`.
Later you will learn how to incorporate information from a reference
genome (if you have one) to improve clustering at this step. For now, bide your
time (but see :ref:`Reference sequence mapping <tutorial_advanced_cli>` if
you're impatient).
Now lets run step 3:
.. code-block:: bash
ipyrad -p params-iptest.txt -s 3
.. parsed-literal::
--------------------------------------------------
ipyrad [v.0.1.73]
Interactive assembly and analysis of RADseq data
--------------------------------------------------
loading Assembly: iptest [~/Documents/ipyrad/tests/iptest.json]
ipyparallel setup: Local connection to 4 Engines
Step3: Clustering/Mapping reads
Saving Assembly.
Again we can examine the results. The stats output tells you how many clusters
were found, and the number of clusters that pass the mindepth thresholds.
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.
.. code-block:: bash
ipyrad -p params-iptest.txt -r
.. parsed-literal::
Summary stats of Assembly iptest
------------------------------------------------
state reads_raw reads_filtered clusters_total clusters_hidepth
1A_0 3 20099 20099 1000 1000
1B_0 3 19977 19977 1000 1000
1C_0 3 20114 20114 1000 1000
1D_0 3 19895 19895 1000 1000
2E_0 3 19928 19928 1000 1000
2F_0 3 19934 19934 1000 1000
2G_0 3 20026 20026 1000 1000
2H_0 3 19936 19936 1000 1000
3I_0 3 20084 20084 1000 1000
3J_0 3 20011 20011 1000 1000
3K_0 3 20117 20117 1000 1000
3L_0 3 19901 19901 1000 1000
Full stats files
------------------------------------------------
step 1: ./iptest_fastqs/s1_demultiplex_stats.txt
step 2: ./iptest_edits/s2_rawedit_stats.txt
step 3: ./iptest_clust_0.85/s3_cluster_stats.txt
step 4: None
step 5: None
step 6: None
step 7: None
The aligned clusters (stacks) found during this step are now located in
``./iptest_clust_0.85/``. You can get a feel for what this looks like
by examining a portion of one of the files using the command below.
.. code-block:: bash
## Same as above, gunzip -c means print to the screen and
## `head -n 28` means just show me the first 28 lines. If
## you're interested in what more of the loci look like
## you can increase the number of lines you ask head for,
## e.g. ... | head -n 100
gunzip -c iptest_clust_0.85/1A_0.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 first
cluster below there is clearly one allele (homozygote) and one read with a
(simulated) sequencing error. For the second cluster it seems there are two alleles
(heterozygote), and a read with a sequencing error. For the third
cluster it's a bit harder to say. Is this 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.
.. parsed-literal::
>1A_0_1164_r1;size=16;*0
TGCAGCTATTGCGACAAAAACACGACGGCTTCCGTGGGCACTAGCGTAATTCGCTGAGCCGGCGTAACAGAAGGAGTGCACTGCCACGTGCCCG
>1A_0_1174_r1;size=1;+1
TGCAGCTATTGCGACAAAAACACGACGGCTTCCGTGGGCACTAGCGTAATTCGCTGAGCCGGCGTAACAGAAGGAGTGCACTGCCACATGCCCG
//
//
>1A_0_4137_r1;size=10;*0
TGCAGGGTCGCCGGCAACTCAGCATTTTAACTCCGCGGGTTACACGTGCGGAGGCCTACTGGCTATCATTTTTAGGGTGCATTTGGTCGGCTGG
>1A_0_4130_r1;size=6;+1
TGCAGGGTCGCCGGCAACTCAGCATTTTAACTCCGCGGGTTACACGTGTGGAGGCCTACTGGCTATCATTTTTAGGGTGCATTTGGTCGGCTGG
>1A_0_4131_r1;size=1;+2
TGCAGGGTCGCCGGCAACTCAGCATTTTAACTCCGCGGGTTACACGTGTCGAGGCCTACTGGCTATCATTTTTAGGGTGCATTTGGTCGGCTGG
//
//
>1A_0_6246_r1;size=15;*0
TGCAGATACAAAAGCTTGCCCACTAAGTTGTGTGATCACTGTCTTATTACGGTGGCCTCCTTCAAGCTTCGAACGAGTTGTGGATCGGTAGGCT
>1A_0_6259_r1;size=1;+1
TGCAGATACAAAAGCTTGCCCACTAAGTTGTGTGATCACTGTCTTATTACGGTGGCCTCCTTCAAGCTTCGAACGAGTTGTGGATCGGGAGGCT
>1A_0_6264_r1;size=1;+2
TGCAGATACAAAAGCTTGCCCACTAAGTTGTGTGATCACTGTCTTATTACGGTGGCCTCCTACAAGCTTCGAACGAGTTGTGGATCGGTAGGCT
>1A_0_6268_r1;size=1;+3
TGCAGATTCAAAAGCTTGCCCACTAAGTTGTGTGATCACTGTCTTATTACGGTGGCCTCCTTCAAGCTTCGAACGAGTTGTGGATCGGTAGGCT
//
//
Step 4: Joint estimation of heterozygosity and error rate
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Jointly estimate sequencing error rate and heterozygosity to help us figure
out which reads are "real" and which are sequencing error. We need to know
which reads are "real" because in diploid organisms there are a maximum of 2
alleles at any given locus. If we look at the raw data and there are 5 or
ten different "alleles", and 2 of them are very high frequency, and the rest
are singletons then this gives us evidence that the 2 high frequency alleles
are good reads and the rest are probably not.
This step is pretty straightforward, and pretty fast. Run it thusly:
.. code-block:: bash
ipyrad -p params-iptest.txt -s 4
.. parsed-literal::
--------------------------------------------------
ipyrad [v.0.1.73]
Interactive assembly and analysis of RADseq data
--------------------------------------------------
loading Assembly: iptest [~/Documents/ipyrad/tests/iptest.json]
ipyparallel setup: Local connection to 4 Engines
--------------------------------------------------
Step4: Joint estimation of error rate and heterozygosity
Saving Assembly.
In terms of results, there isn't as much to look at as in previous steps, though
you can invoke the ``-r`` flag to see the estimated heterozygosity and error
rate per sample.
.. code-block:: bash
ipyrad -p params-iptest.txt -r
.. parsed-literal::
Summary stats of Assembly iptest
------------------------------------------------
state reads_raw reads_filtered clusters_total clusters_hidepth
1A_0 4 20099 20099 1000 1000
1B_0 4 19977 19977 1000 1000
1C_0 4 20114 20114 1000 1000
1D_0 4 19895 19895 1000 1000
2E_0 4 19928 19928 1000 1000
2F_0 4 19934 19934 1000 1000
2G_0 4 20026 20026 1000 1000
2H_0 4 19936 19936 1000 1000
3I_0 4 20084 20084 1000 1000
3J_0 4 20011 20011 1000 1000
3K_0 4 20117 20117 1000 1000
3L_0 4 19901 19901 1000 1000
hetero_est error_est
1A_0 0.002223 0.000756
1B_0 0.001910 0.000775
1C_0 0.002260 0.000751
1D_0 0.001876 0.000731
2E_0 0.001809 0.000770
2F_0 0.002103 0.000725
2G_0 0.001910 0.000707
2H_0 0.002215 0.000755
3I_0 0.001877 0.000784
3J_0 0.001698 0.000741
3K_0 0.001821 0.000767
3L_0 0.002003 0.000755
Full stats files
------------------------------------------------
step 1: ./iptest_fastqs/s1_demultiplex_stats.txt
step 2: ./iptest_edits/s2_rawedit_stats.txt
step 3: ./iptest_clust_0.85/s3_cluster_stats.txt
step 4: ./iptest_clust_0.85/s4_joint_estimate.txt
step 5: None
step 6: None
step 7: None
Step 5: Consensus base calls
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Step 5 uses the inferred error rate and heterozygosity to call the consensus
of sequences within each cluster. Here we are identifying what we believe
to be the real haplotypes at each locus within each sample.
.. code-block:: bash
ipyrad -p params-iptest.txt -s 5
.. parsed-literal::
--------------------------------------------------
ipyrad [v.0.1.73]
Interactive assembly and analysis of RADseq data
--------------------------------------------------
loading Assembly: iptest [~/Documents/ipyrad/tests/iptest.json]
ipyparallel setup: Local connection to 4 Engines
Step5: Consensus base calling
Diploid base calls and paralog filter (max haplos = 2)
error rate (mean, std): 0.00075, 0.00002
heterozyg. (mean, std): 0.00198, 0.00018
Saving Assembly.
Again we can ask for the results:
.. code-block:: bash
ipyrad -p params-iptest.txt -r
And here the important information is the number of ``reads_consens``. This is
the number of "good" reads within each sample that we'll send on to the next step.
As you'll see in examples with empirical data, this is often a step where many
reads are filtered out of the data set. If not data were filtered, then the
number of reads_consens should be equal to the number of clusters_hidepth.
.. parsed-literal::
Summary stats of Assembly iptest
------------------------------------------------
state reads_raw reads_filtered clusters_total clusters_hidepth
1A_0 5 20099 20099 1000 1000
1B_0 5 19977 19977 1000 1000
1C_0 5 20114 20114 1000 1000
1D_0 5 19895 19895 1000 1000
2E_0 5 19928 19928 1000 1000
2F_0 5 19934 19934 1000 1000
2G_0 5 20026 20026 1000 1000
2H_0 5 19936 19936 1000 1000
3I_0 5 20084 20084 1000 1000
3J_0 5 20011 20011 1000 1000
3K_0 5 20117 20117 1000 1000
3L_0 5 19901 19901 1000 1000
hetero_est error_est reads_consens
1A_0 0.002223 0.000756 1000
1B_0 0.001910 0.000775 1000
1C_0 0.002260 0.000751 1000
1D_0 0.001876 0.000731 1000
2E_0 0.001809 0.000770 1000
2F_0 0.002103 0.000725 1000
2G_0 0.001910 0.000707 1000
2H_0 0.002215 0.000755 1000
3I_0 0.001877 0.000784 1000
3J_0 0.001698 0.000741 1000
3K_0 0.001821 0.000767 1000
3L_0 0.002003 0.000755 1000
Full stats files
------------------------------------------------
step 1: ./iptest_fastqs/s1_demultiplex_stats.txt
step 2: ./iptest_edits/s2_rawedit_stats.txt
step 3: ./iptest_clust_0.85/s3_cluster_stats.txt
step 4: ./iptest_clust_0.85/s4_joint_estimate.txt
step 5: ./iptest_consens/s5_consens_stats.txt
step 6: None
step 7: None
Step 6: Cluster across samples
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Step 6 clusters consensus sequences across samples. Now that we have good
estimates for haplotypes within samples we can try to identify similar sequences
at each locus between samples. We use the same clustering threshold as step 3
to identify sequences between samples that are probably sampled from the
same locus, based on sequence similarity.
.. code-block:: bash
ipyrad -p params-iptest.txt -s 6
.. parsed-literal::
--------------------------------------------------
ipyrad [v.0.1.73]
Interactive assembly and analysis of RADseq data
--------------------------------------------------
loading Assembly: iptest [~/Documents/ipyrad/tests/iptest.json]
ipyparallel setup: Local connection to 4 Engines
Step6: Clustering across 12 samples at 0.85 similarity
This step differs from previous steps in that we are no longer applying the
steps to each Sample individually, but instead we are applying a function
to all of the Samples collectively. Our end result is that we now have a
map telling us which loci cluster together from which Samples. This step
does not produce a stats output. It might be more enlightening to consider
the output of step 6 by examining the file that contains the aligned
reads clustered across samples:
.. code-block:: bash
gunzip -c iptest_consens/iptest_catclust.gz | head -n 30 | less
The final output of step 6 is a file in ``iptest_consens/`` called
``iptest_catclust.gz``. There is actually also a database file called
``iptest_test.hdf5`` which records not only the clustered sequences but also
information about read depths at each site, but that file is not
human-readable. The file ``iptest_catclust.gz`` on the other hand provides
a good sanity check for examining the aligned reads across all samples.
Executing the above command you'll see the output below which
shows all the reads that align at one particular locus. You'll see the
sample name of each read followed by the sequence of the read at that locus
for that sample. If you wish to examine more loci you can increase the number
of lines you want to view by increasing the value you pass to ``head`` in
the above command (e.g. ``... | head -n 300 | less``
.. parsed-literal::
1C_0_691
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGAGTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
3L_0_597
TGCAGGGTGGGTKGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTGTAATCGAGTATTAGCGCGGAAGC
2E_0_339
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
2F_0_994
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
3K_0_941
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
1B_0_543
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
3J_0_357
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
2H_0_106
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
3I_0_202
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTACTAGCGCGGAAGC
2G_0_575
TGCAGSGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGKTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
1D_0_744
TGCAGGGTGGGTGGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
1A_0_502
TGCAGGGTGGGTTGTGTTATTTAACATCCAATGCTTAAAGTTTCGATTAGGGGCCTGTTACCGTAGAGTTTTAATCGAGTATTAGCGCGGAAGC
//
//
Step 7: Filter and write output files
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The final step is to filter the data and write output files in many
convenient file formats. First we apply filters for maximum number of
indels per locus, max heterozygosity per locus, max number of snps
per locus, and minimum number of samples per locus. All these filters
are configurable in the params file and you are encouraged to explore
different settings, but the defaults are quite good and quite conservative.
After running step 7 like so:
.. code-block:: bash
ipyrad -p params-iptest.txt -s 7
A new directory is created called ``iptest_outfiles``. This directory contains
all the output files specified in the params file. The default is to
create all supported output files which include
.phy, .nex, .geno, .str, as well as many others (forthcoming). Explore each of
these files as you wish.
Congratulations! You've completed your first toy assembly. Now you can try applying
what you've learned to assemble your own real data. Please consult the docs for many
of the more powerful features of ipyrad including reference sequence mapping,
assembly branching, and post-processing analysis including svdquartets and
many population genetic summary statistics.
