# A simple alignment and quantitation workflow¶

This tutorial covers a rudimentary workflow for aligning reads and doing some QC on the dataset. It assumes that the genome has been set up, as described in Setting up a genome for analysis.

Note

This tutorial is very much under consruction.

This document includes the following sections:

## Preflight checks¶

### Remove cloning adaptors, if present¶

This stage is now frequently taken care of by sequencing facilities. But, if not, you may need to remove untrimmed adaptor sequences manually.

Good options for this include:

• the fastx_clipper utility from the fastx toolkit
• Trimmomatic, which has nice support for paired-end reads

Exact options will depend upon the cloning strategies used in your library prep. Please refer to their documentation as needed.

A number of packages exist for checking the quality of the raw data themselves, and are useful, for example, for spotting a failed cycle of sequencing. The Illumina pipeline creates HTML reports for a number of useful metrics during basecalling. Your sequencing facility can provide these to you.

If not, FastQC produces useful plots of base-wise quality scores, read length distributions, and nucleotide distributions. Its documentation contains examples of what you should expect to see if things work out.

## Perform alignments¶

### Prealignment against rRNA¶

If working on a sample derived from RNA (RNA-seq, ribosome profiling, et c), it is frequently useful to computationally filter out fragments derived from rRNA. Because these are abundant – even after rRNA depletion or polyA selection – removing them saves processing time and disk space downstream.

bowtie is an excellent tool for filtering out rRNA-derived fragments. To use it, one must first obtain or construct a fasta file of rRNA sequences from the organism of interest. These may be obtained from GenBank. Once constructed (in this example, assume it is named species_rRNA.fa), build a bowtie index (in this example, called species_rRNA). From the terminal:

bowtie-build species_rRNA species_rRNA.fa  Then, align the data, discarding any reads mapping to the rRNA, and saving those that do not align to a separate file, here called my_reads_rRNA_unalign.fq:  bowtie -p4 -v3 species_rRNA my_reads.fastq \


Reads that do not align (in my_reads_rRNA_unalign.fq), may then be used as input for additional stages of prealignment, or as input for chromosomal alignment.

### Other uses of (pre)alignment¶

Depending upon how you intend to analyze your data, it may be useful to separate out other classes of reads with additional stages of prealignment:

• To remove expected sequence contaminants:

For example, flies eat yeast. But, often, if we’re performing an experiment on flies, we aren’t too concerned with which genes the yeast are expressing. In this case, it is useful to make a bowtie index of the yeast transcriptome, and filter those reads out, just as we did with the rRNA above.

• To measure transposon expression:

Because transposons exist throughout the genome in multiple, quite similar copies, they produce multimapping reads.

If retrotransposon activity is of interest, one way to capture it is to make a database of consensus sequences of each type of transposon in your genome of interest, and to pre-align to it allowing a generous number of mismatches. Instead of discaring the reads (as we did with rRNA), retain the alignments, using them for expression quantitation.

As previously, take all the reads which did not align in the pre-alignment stages, and put them into chromosomal alignment, below.

### Chromosomal alignment¶

Having filtered out reads derived from rRNA, we can align the remaining data to the genome. In this example, we use Tophat, because it is capable of performing spliced (and other gapped) alignments. We’ll use splice junctions from the juncs file made in the genome setup tutorial.

# run tophat
tophat -o my_chr_alignments \ --bowtie1 \ --raw-juncs splice_junctions.juncs \ --no-novel-juncs \ /path/to/chromosme/bowtie/index \ my_reads_rRNA_unalign.fq # rename output file mv my_chr_alignments/accepted_hits.bam chr_alignments.bam

# index the BAM file for use in plastid, IGV, et c
\$ samtools index chr_alignments.bam


Note

Selection of chromosomal alignment parameters is a complex topic, and beyond the scope of this tutorial.

For more details, see manual for Tophat (or your favorite aligner) and other forums dedicated to this purpose. It is important to choose alignment parameters tailored to your own needs.

## Analysis¶

### Special considerations for ribosome profiling¶

If analyzing ribosome profiling data, it is helpful to estimate the location of the ribosomal P-site and to analyze the sub-codon phasing as quality control metrics.

To do so, make sure you have generated maximal spanning windows from your genome annotation using the metagene script’s generate subprogram (see the metagene tutorial and the metagene script documentation for details).

The output from metagene generate can then be used for P-site estimation and analysis of sub-codon phasing.

#### P-site estimation¶

Estimation of ribosomal P-site offsets is important for all subsequent position-wise analyses in ribosomal profiling (discussed in [IGNW09]).

See Determine P-site offsets for ribosome profiling data for background and step-by-step instructions.

#### Sub-codon phasing¶

After determining P-site offsets, it is possible to examine the sub-codon phasing found in your ribosome profiling data. See Read phasing in ribosome profiling for step-by-step instructions and information on the phase_by_size script.

### Read counting, gene expression, and differential expression analysis¶

This is a large topic, and the details of how to do this depend upon your experiment. One workflow we like is to:

1. Use cs to create a corrected genome annotation
2. Use cs to count the number of reads aligning to each gene in each dataset
3. Combine relevant columns the output from cs from each sample into a single master table
4. Using the master table from (3), perform some clustering to make sure samples behave as expected. For example, replicates should cluster together, while differently treated samples might not. We don’t have a tutorial on this right now, but DESeq2 has a fabulous tutorial on this on its home page, in its vignettes. We recommend you check it out!
5. Use DESeq2 to perform differential expression analysis and test for significance

A step-by-step discussion of a similar workflow, using the simpler counts_in_region script instead of cs appears in Gene expression analysis. At present, steps (1-3) and (5) are discussed.

## Visualization in genome browsers¶

Modern genome browsers can import BAM files of alignments directly. By default, they tend to plot individual alignments and a summary track of read coverage over each nucleotide.

Frequently it is useful to plot some aspect of the data, rather than raw read coverage, as a function of nucleotide position in the genome. For example, in ribosome profiling data, it is useful to plot the total number of ribosomes translating a particular nucleotide.

This data can be extracted from read alignments by use of mapping rules. See Read mapping functions for a further discussion which mapping rules are available, and how to use them.

To export transformed data as a browser track in bedGraph or wiggle formats, see the make_wiggle script. If working on a large (e.g. plant or metazoan) genome, it might be helpful to convert the output from make_wiggle into to a BigWig file, using Jim Kent’s utilities from UCSC.