3. Inspecting and Manipulating Text Data with Unix Tools - Part 1¶

Lesson Objectives

Inspect file/s with utilities such as head,less.
Extracting and formatting tabular data.
Magical grep
use sort, uniq, join to manipulate the one or multiple files at once

Many formats in bioinformatics are simple tabular plain-text files delimited by a character. The most common tabular plain-text file format used in bioinformatics is tab-delimited. Bioinformatics evolved to favor tab-delimited formats because of the convenience of working with these files using Unix tools.

Tabular Plain-Text Data Formats

Tabular plain-text data formats are used extensively in computing. The basic format is incredibly simple: each row (also known as a record) is kept on its own line, and each column (also known as a field) is separated by some delimiter. There are three flavors you will encounter: tab-delimited, comma-separated, and variable space-delimited.

Recap - Inspecting data with head and tail

Although cat command is an easy way for us to open and view the content of a file, it is not very practical to do so for a file with thousands of lines as it will exhaust the shell "space". Instead, large files should be inspected first and then manipulated accordingly. The first round of inspection can be done with head and tail command which prints the first 10 lines and the last 10 lines (-n 10) of a a file, respectively. Let's use head and tail to inspect Mus_musculus.GRCm38.75_chr1.bed

code

head Mus_musculus.GRCm38.75_chr1.bed

Output

1   3054233 3054733
1   3054233 3054733
1   3054233 3054733
1   3102016 3102125
1   3102016 3102125
1   3102016 3102125
1   3205901 3671498
1   3205901 3216344
1   3213609 3216344
1   3205901 3207317

code

tail Mus_musculus.GRCm38.75_chr1.bed

Output

1   195166217   195166390
1   195165745   195165851
1   195165748   195165851
1   195165745   195165747
1   195228278   195228398
1   195228278   195228398
1   195228278   195228398
1   195240910   195241007
1   195240910   195241007
1   195240910   195241007

Changing the number of lines printed for either of those commands can be done by passing -n <number_of_lines> flag .i.e. Over-ride the -n 10 default

Try those commands with -n 4 to print top 4 lines and bottom 4 lines

head -n 4 Mus_musculus.GRCm38.75_chr1.bed

tail -n 4 Mus_musculus.GRCm38.75_chr1.bed

Exercise 4.1

Sometimes it’s useful to see both the beginning and end of a file — for example, if we have a sorted BED file and we want to see the positions of the first feature and last feature. Can you figure out a way to use both head and tail in a single command to inspect first and last 2 lines of Mus_musculus.GRCm38.75_chr1.bed?

solution

(head -n 2; tail -n 2) < Mus_musculus.GRCm38.75_chr1.bed

Exercise 4.2

We can also use tail to remove the header of a file. Normally the -n argument specifies how many of the last lines of a file to include, but if -n is given a number x preceded with a + sign (e.g., +x ), tail will start from the x^th line. So to chop off a header, we start from the second line with -n+2.
Use the seq command to generate a file containing the numbers 1 to 10, and then use the tail command to chop off the first line.

solution

seq 10 > nums.txt

cat nums.txt

tail -n+2 nums.txt

Extract summary information with `wc`¶

The "wc" in the wc command which stands for "word count" - this command can count the numbers of lines, words, and characters in a file (take a note on the order).

code

wc Mus_musculus.GRCm38.75_chr1.bed 

  81226  243678 1698545 Mus_musculus.GRCm38.75_chr1.bed

Often, we only need to list the number of lines, which can be done by using the -l flag. It can be used as a sanity check - for example, to make sure an output has the same number of lines as the input, OR to check that a certain file format which depends on another format without losing overall data structure wasn't corrupted or over/under manipulated.

Question

Count the number of lines in Mus_musculus.GRCm38.75_chr1.bed and Mus_musculus.GRCm38.75_chr1.gtf . Anything out of the ordinary ?

Although wc -l is the quickest way to count the number of lines in a file, it is not the most robust as it relies on the very bad assumption that "data is well formatted"

For an example, if we are to create a file with 3 rows of data and then two empty lines by pressing Enter twice,

code

cat > fool_wc.bed

  1 100
  2 200
  3 300

Ctrl+D to end the edits started with cat >

code

wc -l fool_wc.bed

  5 fool_wc.bed

This is a good place to bring in grep again which can be used to count the number of lines while excluding white-spaces (spaces, tabs (t) or newlines (n))

code

grep -c "[^ \n\t]" fool_wc.bed

Using `cut` with column data and formatting tabular data with `column`¶

When working with plain-text tabular data formats like tab-delimited and CSV files, we often need to extract specific columns from the original file or stream. For example, suppose we wanted to extract only the start positions (the second column) of the Mus_musculus.GRCm38.75_chr1.bed file. The simplest way to do this is with cut.

code

cut -f 2 Mus_musculus.GRCm38.75_chr1.bed | head -n 3

3054233
3054233
3054233

-f argument is how we specify which columns to keep. It can be used to specify a range as well

code

cut -f 2-3 Mus_musculus.GRCm38.75_chr1.bed | head -n 3

 3054233   3054733
 3054233   3054733
 3054233   3054733

Using cut, we can convert our GTF for Mus_musculus.GRCm38.75_chr1.gtf to a three-column tab-delimited file of genomic ranges (e.g., chromosome, start, and end position). We’ll chop off the metadata rows using the grep command covered earlier and then use cut to extract the first, fourth, and fifth columns (chromosome, start, end):

code

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | cut -f 1,4,5 | head -n 3

1  3054233 3054733
1  3054233 3054733
1  3054233 3054733

Note that although our three-column file of genomic positions looks like a BED-formatted file, it’s not (due to subtle differences in genomic range formats).

cut also allows us to specify the column delimiter character. So, if we were to come across a CSV file containing chromosome names, start positions, and end positions, we could select columns from it, too:

As you may have noticed when working with tab-delimited files, it’s not always easy to see which elements belong to a particular column. For example:

code

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | cut -f 1-8 | head -n 3

  1    pseudogene  gene    3054233 3054733 .   +   .
  1    unprocessed_pseudogene  transcript  3054233 3054733 .   +   .
  1    unprocessed_pseudogene  exon    3054233 3054733 .   +   .

While tabs are a great delimiter in plain-text data files, our variable width data causes our columns to not stack up well. There’s a fix for this in Unix: program column -t (the -t option tells column to treat data as a table). The column -t command produces neat columns that are much easier to read:

code

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | cut -f 1-8 | column -t | head -n 3

   1  pseudogene                          gene         3054233    3054733    .  +  .
   1  unprocessed_pseudogene              transcript   3054233    3054733    .  +  .
   1  unprocessed_pseudogene              exon         3054233    3054733    .  +  .

Note that you should only use column -t to visualize data in the terminal, not to reformat data to write to a file. Tab-delimited data is preferable to data delimited by a variable number of spaces, since it’s easier for programs to parse. Like cut , column’s default delimiter is the tab character (\t ). We can specify a different delimiter with the -s option. So, if we wanted to visualize the columns of the Mus_musculus.GRCm38.75_chr1_bed.csv file more easily, we could use:

code

column -s "," -t Mus_musculus.GRCm38.75_chr1_bed.csv | head -n 3

    1   3054233 3054733
    1   3054233 3054733
    1   3054233 3054733

Counting the number of columns of a .gtf with grep and wc

GTF Format has 9 columns

1	2	3	4	5	6	7	8	9
seqname	source	feature	start	end	score	strand	frame	attribute

In theory, we should be able to use the following command to isolate the column headers and count the number

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | head -1 | wc -w

However, this will return 16 and the problem is with the attribute column which is a "A semicolon-separated list of tag-value pairs, providing additional information about each feature."

gene_id "ENSMUSG00000090025"; gene_name "Gm16088"; gene_source "havana"; gene_biotype "pseudogene";

Therefore, we need to translate* these values to a single "new line" with tr command and then count the number of lines than than the number of words

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | head -1 | tr '\t' '\n' | wc -l

Sorting Plain-Text Data with `sort`¶

Very often we need to work with sorted plain-text data in bioinformatics. The two most common reasons to sort data are as follows:

Certain operations are much more efficient when performed on sorted data.
Sorting data is a prerequisite to finding all unique lines.

sort is designed to work with plain-text data with columns. Create a test .bed file with few rows and use sort command without any arguments.

code

cat > test_sort.bed

input

chr1    26  39
chr1    32  47
chr3    11  28
chr1    40  49
chr3    16  27
chr1    9   28
chr2    35  54
chr1    10  19

code

sort test_sort.bed

Output

chr1    10  19
chr1    26  39
chr1    32  47
chr1    40  49
chr1    9   28
chr2    35  54
chr3    11  28
chr3    16  27

sort without any arguments simply sorts a file alphanumerically by line. Because chromosome is the first column, sorting by line effectively groups chromosomes together, as these are "ties" in the sorted order.

However, using sort’s default of sorting alphanumerically by line and doesn’t handle tabular data properly. There are two new features we need:

The ability to sort by particular columns
The ability to tell sort that certain columns are numeric values (and not alpha‐numeric text)

sort has a simple syntax to do this. Let’s look at how we’d sort example.bed by chromosome (first column), and start position (second column):

code

sort -k1,1 -k2,2n test_sort.bed

Output

chr1    9   28
chr1    10  19
chr1    26  39
chr1    32  47
chr1    40  49
chr2    35  54
chr3    11  28
chr3    16  27

Here, we specify the columns (and their order) we want to sort by as -k arguments. In technical terms, -k specifies the sorting keys and their order. Each -k argument takes a range of columns as start,end, so to sort by a single column we use start,start. In the preceding example, we first sorted by the first column (chromosome), as the first -k argument was -k1,1 . Sorting by the first column alone leads to many ties in rows with the same chromosomes (e.g., “chr1” and “chr3”). Adding a second -k argument with a different column tells sort how to break these ties. In our example, -k2,2n tells sort to sort by the second column (start position), treating this column as numerical data (because there’s an n in -k2,2n).

The end result is that rows are grouped by chromosome and sorted by start position.

Exercise 4.3

Mus_musculus.GRCm38.75_chr1_random.gtf file is Mus_musculus.GRCm38.75_chr1.gtf with permuted rows (and without a metadata header). Can you group rows by chromosome, and sort by position? If yes, append the output to a separate file.

solution

sort -k1,1 -k4,4n Mus_musculus.GRCm38.75_chr1_random.gtf > Mus_musculus.GRCm38.75_chr1_sorted.gtf

Finding Unique Values with `uniq`¶

uniq takes lines from a file or standard input stream and outputs all lines with consecutive duplicates removed. While this is a relatively simple functionality, you will use uniq very frequently in command-line data processing.

code

cat letters.txt

Output

A 
A
B
C
B
C
C
C

code

uniq letters.txt

A
B
C
B
C

As you can see, uniq does not return the unique values in letters.txt — it only removes consecutive duplicate lines (keeping one). If instead we did want to find all unique lines in a file, we would first sort all lines using sort so that all identical lines are grouped next to each other, and then run uniq.

code

sort letters.txt | uniq

A
B
C

uniq with -c shows the counts of occurrences next to the unique lines.

code

sort letters.txt | uniq -c

      2 A
      2 B
      4 C

Combined with other Unix tools like cut, grep and sort, uniq can be used to summarize columns of tabular data:

code

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | cut -f3 | sort | uniq -c

Output

25901 CDS
36128 exon
2027 gene
2290 start_codon
2299 stop_codon
4993 transcript
7588 UTR

Count in order from most frequent to last

code

grep -v "^#" Mus_musculus.GRCm38.75_chr1.gtf | cut -f3 | sort | uniq -c | sort -rn

  36128 exon
  25901 CDS
   7588 UTR
   4993 transcript
   2299 stop_codon
   2290 start_codon
   2027 gene

n and r represents numerical sort and reverse order (Or descending as the default as ascending)

Joining two files with `join`¶

Prepare

Create a file named example_lengths.txt with following content

chr1    58352
chr2    39521
chr3    24859

The Unix tool join is used to join different files together by a common column. For example, we may want to add chromosome lengths recorded in example_lengths.txt to example.bed BED file, we saw earlier. The files look like this:

code

cat example.bed
echo "=========="
cat example_lengths.txt

chr1   26  39
chr1   32  47
chr3   11  28
chr1   40  49
chr3   16  27
chr1   9   28
chr2   35  54
chr1   10  19
==========
chr1   58352
chr2   39521
chr3   24859

To do this, we need to join both of these tabular files by their common column, the one containing the chromosome names. But first, we first need to sort both files by the column to be joined on (join would not work otherwise):

code

sort -k1,1 example.bed > example_sorted.bed

sort -c -k1,1 example_lengths.txt

-c, --check, --check=diagnose-first = check for sorted input; do not sort

The basic syntax is join -1 <file_1_field> -2 <file_2_field> <file_1> <file_2>. So, with example.bed and example_lengths.txt this would be:

code

join -1 1 -2 1 example_sorted.bed example_lengths.txt > example_with_lengths.txt

cat example_with_lengths.txt

There are many types of joins. For now, it’s important that we make sure join is working as we expect. Our expectation is that this join should not lead to fewer rows than in our example.bed file. We can verify this with wc -l:

code

wc -l example_sorted.bed example_with_lengths.txt

  8 example_sorted.bed
  8 example_with_lengths.txt
 16 total

However, look what happens if our second file, example_lengths.txt doesn’t have the lengths for chr3:

code

head -n 2 example_lengths.txt > example_lengths_alt.txt #truncate file

join -1 1 -2 1 example_sorted.bed example_lengths_alt.txt

chr1 10 19 58352
chr1 26 39 58352
chr1 32 47 58352
chr1 40 49 58352
chr1 9 28 58352
chr2 35 54 39521

code

join -1 1 -2 1 example_sorted.bed example_lengths_alt.txt | wc -l

Because chr3 is absent from example_lengths_alt.txt, our join omits rows from example_sorted.bed that do not have an entry in the first column of example_lengths_alt.txt. If we don’t want this behavior, we can use option -a to include unpairable lines—ones that do not have an entry in either file:

code

join -1 1 -2 1 -a 1 example_sorted.bed example_lengths_alt.txt

-a FILENUM - also print unpairable lines from file FILENUM, where FILENUM is 1 or 2, corresponding to FILE1 or FILE2

chr1 10 19 58352
chr1 26 39 58352
chr1 32 47 58352
chr1 40 49 58352
chr1 9 28 58352
chr2 35 54 39521
chr3 11 28
chr3 16 27

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3. Inspecting and Manipulating Text Data with Unix Tools - Part 1¶

Extract summary information with wc¶

Using cut with column data and formatting tabular data with column¶

Sorting Plain-Text Data with sort¶

Finding Unique Values with uniq¶

Joining two files with join¶

Extract summary information with `wc`¶

Using `cut` with column data and formatting tabular data with `column`¶

Sorting Plain-Text Data with `sort`¶

Finding Unique Values with `uniq`¶

Joining two files with `join`¶