# LAST Tutorial

LAST finds similar regions between sequences, and aligns them.

## Example 1: Compare the human and fugu mitochondrial genomes

For our first example, we wish to find and align similar regions between the human and fugu mitochondrial genomes. You can find these sequences in the examples directory: humanMito.fa and fuguMito.fa. We can compare them like this:

    lastdb -cR01 humdb humanMito.fa
    lastal humdb fuguMito.fa > myalns.maf

The lastdb command creates several files whose names begin with "humdb". The lastal command then compares fuguMito.fa to the humdb files, and writes the alignments to a file called "myalns.maf".

The "-cR01" option suppresses alignments caused by simple sequence such as cacacacacacacacacacacaca.

## Understanding the output

The output has very long lines, so you need to view it without line-wrapping. For example, with a Unix/Linux/MacOsX command line, you can use:

    less -S myalns.maf

Each alignment looks like this:

    a score=27 EG2=4.7e+04 E=2.6e-05
    s humanMito 2170 145 + 16571 AGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTT...
    s fuguMito  1648 142 + 16447 AGTAGGCTTAGAAGCAGCCACCA--CAAGAAAGCGTT...

The score is a measure of how strong the similarity is. EG2 and E are explained at last-evalues.html. Lines starting with "s" contain: the sequence name, the start coordinate of the alignment, the number of bases spanned by the alignment, the strand, the sequence length, and the aligned bases.

The start coordinates are zero-based. This means that, if the alignment begins right at the start of a sequence, the coordinate is 0. If the strand is "-", the start coordinate is in the reverse strand.

This alignment format is called MAF (multiple alignment format). You can convert it to several other formats using maf-convert. You can make lastal produce a few other formats with option -f (see lastal.html).

## Example 2: Compare vertebrate proteins to invertebrate proteins

Use the lastdb -p option to indicate that the sequences are proteins:

    lastdb -p -cR01 invdb invertebrate.fa
    lastal invdb vertebrate.fa

## Example 3: Compare DNA sequences to protein sequences

Here we use the -F15 option, to specify translated alignment with a score penalty of 15 for frameshifts:

    lastdb -p -cR01 protdb proteins.fa
    lastal -F15 protdb dnas.fa

## Example 4: Find short protein alignments

LAST uses a scoring scheme to find similarities. Some scoring schemes are good for long-and-weak similarities, others for short-and-strong similarities. If we seek very short similarities, weak ones are hopeless (statistically insignificant), so we had better focus on strong ones. The PAM30 scoring scheme may work well:

    lastdb -p -cR01 invdb invertebrate.fa
    lastal -pPAM30 invdb vertebrate.fa

(How short is "very short"? It depends on the amount of sequence data we are searching, but perhaps roughly less than 40 amino acids.)

## Example 5: Align human DNA reads to the human genome

Suppose we have DNA reads in a file called reads.fastq, in fastq-sanger format. We can align them to the human genome like this:

    lastdb -uNEAR humandb human/chr*.fa
    lastal -Q1 -e120 humandb reads.fastq | last-split > myalns.maf

This will use about 15 gigabytes of memory.

- -uNEAR selects a seeding scheme that makes it better at finding short-and-strong similarities. (It also changes the default scoring scheme.)

- The -Q1 option indicates that the reads are in fastq-sanger format.

- The -e120 option requests alignments with score ≥ 120. This is intentionally a somewhat low score (high E-value): last-split then discards low-confidence alignments, but it uses them to estimate the ambiguity of high-confidence alignments.

- last-split reads the alignments produced by lastal, and looks for a unique best alignment for each part of each read. It allows different parts of one read to match different parts of the genome. It has several useful options, please see last-split.html.

## Paired reads

If you have paired reads, there are two options:

1. Use last-pair-probs (see last-pair-probs.html).

2. Ignore the pairing information, and align the reads individually (using last-split as above). This may be useful because last-pair-probs does not currently allow different parts of one read to match different parts of the genome, though it does allow the two reads in a pair to match (e.g.) different chromosomes.

## Fastq format confusion

Unfortunately, there is more than one fastq format (see http://nar.oxfordjournals.org/content/38/6/1767.long). Recently (2013) fastq-sanger seems to be dominant, but if you have another variant you need to change the -Q option (see lastal.html).

## Example 6: Align human fasta reads to the human genome

If our reads are in fasta instead of fastq format, we simply omit -Q:

    lastdb -uNEAR humandb human/chr*.fa
    lastal -e120 humandb reads.fa | last-split > myalns.maf

(In older versions of LAST, we had to set a short-and-strong scoring scheme, but this is now done automatically by -uNEAR.)

## Example 7: Align aardvark fastq reads to the human genome

In this case we expect weak similarities, so we omit -uNEAR. We also need to change the scoring scheme (because with -Q1 it defaults to a short-and-strong scoring scheme):

    lastdb -cR01 humandb human/chr*.fa
    lastal -Q1 -r5 -q5 -a35 -b5 humandb reads.fastq > myalns.maf

Option -r5 sets the match score to 5, -q5 sets the mismatch cost to 5, while -a35 and -b5 set the gap cost to 35 + 5×(gap length).

(Why use 5:5:35:5 rather than 1:1:7:1? The reason is that 5:5:35:5 has roughly the same scale as the fastq quality scores. lastal uses the quality scores to modify the alignment scores, and then rounds the modified scores to integers. If we used 1:1:7:1, the integer-rounding would lose information.)

## Very short reads

WARNING! The standard score parameters do not align very short reads. This is because the match score is 6 and the score threshold is 120, so at least 20 high-quality matches are required (or a greater number of low-quality matches). In addition, last-split discards low-confidence alignments. To align very short reads, reduce lastal's score threshold (-e) or increase last-split's error threshold (-m).

If the score threshold is too low, you will get meaningless, random alignments.

## Tuning speed, sensitivity, memory and disk usage

- You can make LAST faster by using multiple CPUs.

- You can trade off speed, sensitivity, memory and disk usage.

## Example 8: Compare the cat and rat genomes

If you have ~50 GB of memory and don't mind waiting a few days, this is a good way to compare such genomes:

    lastdb -cR11 -uMAM8 catdb cat.fa
    lastal -m100 -E0.05 catdb rat.fa | last-split -m1 > out.maf

This looks for a unique best alignment for each part of each rat chromosome. Omitting -m100 makes it faster but somewhat less sensitive. Omitting -uMAM8 reduces the memory use to ~10 GB and makes it faster but considerably less sensitive.

This recipe aligns each rat base-pair to at most one cat base-pair, but not necessarily vice-versa. You can get strictly 1-to-1 alignments by swapping the sequences and running last-split again:

    maf-swap out.maf | last-split -m1 > out2.maf

## Example 9: Compare the human and chimp genomes

For strongly similar genomes (e.g. 99% identity), something like this is more appropriate:

    lastdb -cR11 -uNEAR human human.fa
    lastal -m50 -E0.05 human chimp.fa | last-split -m1 > out.maf

## Example 10: Ambiguity of alignment columns

Consider this alignment:

    TGAAGTTAAAGGTATATGAATTCCAATTCTTAACCCCCCTATTAAACGAATATCTTG
    |||||||| ||||||  |  ||  | |  |    || ||||||   |||||||||||
    TGAAGTTAGAGGTAT--GGTTTTGAGTAGT----CCTCCTATTTTTCGAATATCTTG

The middle section has such weak similarity that its precise alignment cannot be confidently inferred.

It is sometimes useful to estimate the ambiguity of each column in an alignment. We can do that using lastal option -j4:

    lastdb -cR01 humdb humanMito.fa
    lastal -j4 humdb fuguMito.fa > myalns.maf

The output looks like this:

    a score=17 EG2=9.3e+09 E=5e-06
    s seqX 0 57 + 57 TGAAGTTAAAGGTATATGAATTCCAATTCTTAACCCCCCTATTAAACGAATATCTTG
    s seqY 0 51 + 51 TGAAGTTAGAGGTAT--GGTTTTGAGTAGT----CCTCCTATTTTTCGAATATCTTG
    p                %*.14442011.(%##"%$$$$###""!!!""""&'(*,340.,,.~~~~~~~~~~~

The "p" line indicates the probability that each column is wrongly aligned, using a compact code (the same as fastq-sanger format):

> Symbol | Error probability | Symbol | Error probability
> `!` | 0.79 -- 1 | `0` | 0.025 -- 0.032
> `"` | 0.63 -- 0.79 | `1` | 0.02 -- 0.025
> `#` | 0.5 -- 0.63 | `2` | 0.016 -- 0.02
> `$` | 0.4 -- 0.5 | `3` | 0.013 -- 0.016
> `%` | 0.32 -- 0.4 | `4` | 0.01 -- 0.013
> `&` | 0.25 -- 0.32 | `5` | 0.0079 -- 0.01
> `'` | 0.2 -- 0.25 | `6` | 0.0063 -- 0.0079
> `(` | 0.16 -- 0.2 | `7` | 0.005 -- 0.0063
> `)` | 0.13 -- 0.16 | `8` | 0.004 -- 0.005
> `*` | 0.1 -- 0.13 | `9` | 0.0032 -- 0.004
> `+` | 0.079 -- 0.1 | `:` | 0.0025 -- 0.0032
> `,` | 0.063 -- 0.079 | `;` | 0.002 -- 0.0025
> `-` | 0.05 -- 0.063 | `<` | 0.0016 -- 0.002
> `.` | 0.04 -- 0.05 | `=` | 0.0013 -- 0.0016
> `/` | 0.032 -- 0.04 | `>` | 0.001 -- 0.0013

Note that each alignment is grown from a "core" region, and the ambiguity estimates assume that the core is correctly aligned. The core is indicated by "~" symbols, and it contains exact matches only.

LAST has options to find alignments with optimal column probabilities, instead of optimal score: see lastal.html.