# Ad hoc bioinformatic analysis in database

Recently I’ve found that bioinformatic analysis in a database is not hard at all and the database set up wasn’t as daunting as it sounds, especially when the data are tabular. I used to start my analysis with loading everything into R or Python, and then figuring out all the filtering and grouping commands with my favorite R or Python packages. However, the data size would be bound by memory and the analysis might be slow unless additional optimization was applied. On the other hand, databases have already solved the problems by mapping the data to disk and indexing. Therefore I’d like to share my recent experience on using databases for bioinfo analysis.

Note that if one is interested in the actual tips of using databases for analysis, feel free to skip the whole background section.

## Background

### Reading tabular data in bioinformatics

Tabular data are everywhere in bioinformatics. To record gene expressions, variants or cross reference IDs between different annotation systems or databases, data are stored in various tabular-like formats, such as BED, GTF, MAF, and VCF, which can usually be normalized to the standard CSV and TSV files. Starting with the raw data, we apply different kinds of filtering and grouping to pick up the records of interest. For example, we might subset the data within a genomic region, select transcripts above an expression threshold, or group the data by the same transcript across multiple samples.

Researchers have developed numerous tools to select the data of interest. In Python, numpy and pandas dominate the analysis; in R, data.frame, tibble, and data.table are all widely used. However, all the tools above only work if the data can be fit into memory. Unfortunately, bioinformatics data can go beyond 10GB easily these days. It has been difficult to analyze everything in memory. Even using a powerful server with a few hundreds GB of memory, the overhead of loading all data into memory can be time-consuming. To make things worse, when joining multiple data together, the magnitude of the issues above will be multiplied.

One might argue that in Python there are packages like xarray and dask capable of handling out-of-memory multi-dimensional array. But they are only useful for handling numerical data. In bioinformatics, metadata are frequently used and consist of many text columns, where numpy doesn’t have the same computing advantage as numerical columns. For example, gene expression only makes sense if it comes with the gene symbol, the transcript id, and the sample id.

### Database

Databases have been solving the out-of-memory data analysis for decades, and it also comes with several advantages. First, the language databases use is standardized, known as Structured Query Language (SQL). SQL is expressive, which means instead of writing how to load or query the data, one writes what the data or the query look like. Databases support concurrent reads, enabling query in parallel. Second, One can speed up the queries by setting up indexes. Different types of indexes and different combinations of columns can be added to boost the query. Lastly, databases are persistent, so one only needs to load the data once.

I mainly use two databases: SQLite and PostgreSQL. SQLite’s database is just a single file on disk and it doesn’t need any configuration to run. In fact SQLite ships with Python, available as the sqlite module. SQLite works very well in my case.

PostgreSQL is a more feature-rich database and has better concurrency support such as multiple writers at the same time. Its advanced indexing and data types might be helpful for genomic range query. The downside is that it requires some configurations and its installation is not as easy as SQLite. Though the basic PostgreSQL setup is actually just a few commands on Debian Linux, one probably needs to go through some documentation to understand what they are about and how to tweak the config.

The most annoying thing I found using a database in the past was to load my data, where I had to create the table by CREATE TABLE ... and insert all my data by multiple INSERT INTO ... VALUES ... statements. But recently I found that many databases have some built-in utilities to make the process easy and fast. Also, it is not hard to programmatically generate the statements through packages like SQLAlchemy. Therefore, I will share some experience of using databases here.

## Tabular data IO in database

### SQLite

For SQLite, use .mode csv with .import statement to load in data. SQLite will create the table automatically by using the first row as the column names if the table doesn’t exist. One can create the table before the loading to define each column’s data type, otherwise, columns are just TEXT type. .separator controls the delimiter character SQLite uses between columns.

.mode csv
.separator \t   -- For TSV files
.import '/path/to/tsv' table_name


To export data, use .once statement followed by the query:

.header on  -- Export columns name
.once '/path/to/output.tsv'
SELECT * FROM table_name;  -- Export all data in the table


Commands above can be scripted into SQLite like:

sqlite3 mydb.sqlite < load_data.sql


### PostgreSQL

For PostgreSQL, the built-in solution is to use the COPY statement or the \copy metacommand to import or export data. COPY runs faster than the equivalent INSERT statements. Besides built-in commands, an external tool pgloader has been very helpful for the data loading, whose loading process is more flexible.

In this post, I won’t dive into details of their usage. There will be an example in the benchmark section.

Many tabular data are compressed by gzip or bgzip to save the disk space. To decompress the file and load into the database without storing the uncompressed file somewhere first, one can consider using named pipe.

The idea is to decompress the file to a named pipe and read the data in a database from the named pipe. A named pipe can be created by mkfifo. For example,

mkfifo mypipe
gunzip -c mydata.tsv.gz > mypipe &


The trailing & makes the decompress command running in the background to keep everything in one shell session. Then read the data in SQLite as if it were a file like:

.import mypipe mytable


The trick here can be further expanded to any preprocessing in any language. One can simply preprocess the file and write the output to a named pipe. The database can read from the named pipe without storing the full intermediate output on disk. Plus, by piping between commands more CPU cores are utilized.

## Benchmark

To give an idea of the data processing time in databases, I used all the somatic variants from TCGA MC3 as a demonstration. The goal here is to count the number of variants by different transcript and its mutation type. So the output result will be something like the following:

Transcript ID Mutation type Count
ENST00000000233 3’UTR 20
ENST00000000233 Frame_Shift_Del 1
ENST00000000233 Intron 6

After filtering out all the silent mutations, there are about total 2.8 million variants making up 614MB of disk space.

I used three methods to load and group the variants: pandas, SQLite, and PostgreSQL. Their code is shown below.

### pandas (Python)

Standard pandas IO code.

import numpy as np
import pandas as pd

'mc3_filtered.tsv',
names=[
'chrom', 'start', 'end', 'strand', 'mutation_type',
'ref_allele', 'alt_allele', 'transcript_id',
'hgvs_c', 'hgvs_p', 'cdna_start', 'cdna_end',
'p_start', 'p_end', 'normal_id', 'tumor_id'
],
dtype={
'chrom': str, 'start': np.int64, 'end': np.int64,
'strand': np.int32, 'cdna_start': str, 'cdna_end': str,
'p_start': str, 'p_end': str,
},
engine='c',
)

grp_df = df.groupby(['transcript_id', 'mutation_type'])['alt_allele'].count().reset_index()
grp_df.to_csv('out.pandas.tsv', index=False, sep='\t')


### SQLite

I set some PRAGMA ... statements at the beginning to control some of the SQLite settings. It tells SQLite to use more cache, create temporary tables in memory and disable all the transaction recovery settings. SQLite by default writes everything to the disk first before changing the actual database content so if the program fails or any exception occurs, it can recover all the transactions properly. In our case, we don’t care about the integrity of the database.

PRAGMA cache_size=-4192000;  -- Use 2GB RAM as cache
PRAGMA temp_store=MEMORY;
PRAGMA synchronous=OFF;
PRAGMA journal_mode=OFF;
PRAGMA locking_mode=EXCLUSIVE;

.mode csv
.separator \t
CREATE TABLE mc3 (
chrom       TEXT,
"start"     INT,
"end"       INT,
strand      INT,
mutation_type   TEXT,
ref_allele  TEXT,
alt_allele  TEXT,
transcript_id   TEXT,
hgvs_c      TEXT,
hgvs_p      TEXT,
cdna_start  INT,
cdna_end    INT,
p_start     INT,
p_end       INT,
normal_id   TEXT,
tumor_id    TEXT
);
.import mc3_filtered.tsv mc3
-- Create an index to speed up grouping on the same columns
CREATE INDEX mc3_idx ON mc3 (transcript_id, mutation_type);

-- Output
.once out.sqlite.tsv
SELECT transcript_id, mutation_type, COUNT(alt_allele) AS c
FROM mc3
GROUP BY transcript_id, mutation_type;


### PostgreSQL

I used pgloader to load the data into a local PostgreSQL database test_mc3. pgloader can take a script of its own mini-language.

LOAD CSV
FROM 'mc3_filtered.tsv'
INTO postgresql:///test_mc3?mc3
WITH fields terminated by '\t',
fields not enclosed,
drop indexes
$$DROP TABLE IF EXISTS mc3;$$,
$$CREATE TABLE mc3 ( chrom TEXT, "start" BIGINT, "end" BIGINT, strand SMALLINT, mutation_type TEXT, ref_allele TEXT, alt_allele TEXT, transcript_id TEXT, hgvs_c TEXT, hgvs_p TEXT, cdna_start INT, cdna_end INT, p_start INT, p_end INT, normal_id TEXT, tumor_id TEXT );$$,
$$CREATE INDEX mc3_idx ON mc3 (transcript_id, mutation_type);$$
;


To do the grouping analysis, I used the built-in COPY command:

COPY (
SELECT transcript_id, mutation_type, COUNT(alt_allele) AS c
FROM mc3
GROUP BY transcript_id, mutation_type
) TO '/private/tmp/mc3/MC3/out.psql.tsv' WITH (FORMAT TEXT);


### Result

I didn’t run it systematically but a few repeats showed the similar numbers.

Method Read data (sec) Group-by analysis (sec)
Pandas 10.7 0.9
SQLite 27.7 4.0
PostgreSQL 82.6 13.5

In this case, all data can be loaded into memory easily, so pandas gave the best performance here. It actually took nearly no-time to complete the grouping.

All databases ran much slower on loading data than pandas. PostgreSQL seems to run a lot more slower than SQLite, which I think it has something to do with my server configuration, say, not enough cache size, or not enough working memory for the group-by operation. I feel like PostgreSQL can be faster but anyway this’s the result I have so far. Note that all the databases are stored on a PCIe SSD disk. If they were on a normal hard drive, the database creation will take a much longer time.

However, after the data are loaded into the database, the speed of the query alone is comparable to pandas. Because for pandas, one cannot skip the step of reading data so if the analysis is on a frequently used dataset, database like SQLite can yield better performance. Once the data get larger than the memory capacity, special care will be needed to make the pandas’ approach work, whereas database can scale up with little fuss.

## Conclusion

My post provides a different solution to work with tabular data by working in a database. In-memory approaches like pandas work very efficiently at a small dataset but one will have to code the “how-tos” to scale to a larger dataset that cannot feed into memory (or the overhead is too high). On the other hand, databases can easily scale to a few hundred GBs in size and the query is fast. For analysis on a frequently used dataset, loading data into the database first might be a good idea.

Another good thing about databases is that SQL makes joining across tables easily. One can easily join across multiple tables, say, expand the gene annotation and doesn’t have to worry how to implement it. With indexing, the joining can be fast. In pandas, one generates many objects representing the joining results, but those objects cannot be easily shared between scripts. Relying on storing the intermediate objects on disk, the accumulated overhead might be significant. Projects like Apache Arrow might solve the in-memory object passing ultimately, but its development is still in the early phase. As for databases, one can define reusable views for the joining logic and filtering results. The post didn’t really touch this part so I probably need another benchmark or post to back my thoughts.

If one is analyzing variants, using databases or SQL in general has been backed up by many pratical projects. People at Quinlab Lab hace been building vcf2db to load variants into databases for downstream annotation and analysis. To scale way up to terabytes or petabytes of variant data, Google Cloud Genomics provides an interface to store and query variants in BigQuery, where users use standard SQL to select the variants of interest.

However, working in pandas gives users great room for flexibility. For example, one can iterate over rows and do some complex transformation of the value. Maybe it would be the optimal solution to use pandas.read_sql to run a query in a database.

It seems to me like many people rely too much on the features of some special file formats such as bgzip and tabix and have forgotten the generic yet flexible approach using databases. Those formats often optimize the random access by a given genomic query by indexing. In databases, such index is analogous to (chrom, start, -end) or even GiST index on Range type in PostgreSQL. It might be slower in databases, but aside from the performance, one can continue to query the records in the same way in databases. For special format, the functionality will be much limited.

Now I will give the database approach a try before writing my own data wrangling script.

EDIT 2018-01-28: Add real world examples of using databases to store variant data.