Deriving the elemental composition from a molformula in pgchem::tigress

pgchem::tigress can generate molecular formulae like C3H6NO2- from chemical structures.

But what if we need access to the elemental composition as a relation, e.g:

element	count
C	3
N	1
O	2

Fortunately, PostgreSQL is awesome:

CREATE OR REPLACE FUNCTION elemental_composition(molformula TEXT)
  RETURNS TABLE(element TEXT, count INTEGER) AS
$BODY$
DECLARE token TEXT[];
DECLARE elements TEXT[];
BEGIN
elements := ARRAY['C','N','O','P','S','Cl']; --expand as needed
molformula := REPLACE(REPLACE(molformula,'-',''),'+','');

FOREACH element IN ARRAY elements LOOP
count := 1;
token := REGEXP_MATCHES(molformula, element || '[\d?]*');

IF (token[1] IS NOT NULL) THEN
    token := REGEXP_MATCHES(token[1],'[0-9]+');
        IF (token[1] iS NOT NULL) THEN
            count := token[1]::INTEGER;
        END IF;
RETURN NEXT;
END IF;
END LOOP;
RETURN;
END;
$BODY$
  LANGUAGE plpgsql IMMUTABLE STRICT
  COST 1000;

SELECT * FROM elemental_composition('C3H6NO2-');

And that's it. Did I already mention that PostgreSQL is awesome? :-)

1st impression of pg_shard

1st impression of pg_shard:

1. It works as described, documented limitations apply
2. Status '3' of a shard_placement means inactive, '1' means online
3. Read the issues list - unless you want to discover them yourselves :-)
4. If a whole worker node goes offline this may go unnoticed, since there seems to be no heartbeat between the head and worker nodes unless you try to write data
5. It already supports range based sharding but you have to activate it manually like so
6. It does not support prepared statements at the moment

nseq - A datatype for the efficient storage of nucleotide sequences in PostgreSQL

The nseq datatype allows to store DNA and RNA sequences consisting of the letters AGCT or AGCU respectively in PostgreSQL.

By encoding four bases per Byte, it uses 75 percent less space on disk than text. While this idea is obvious and far from being novel, it still is one of the most efficient compression schemes for DNA/RNA, if not the most efficient.

As of now, nseq only supports very basic native operations. The main shortcoming is, that it has to be decompressed into text hence expanding by 4x, for e. g. substring operations and the like.

This will change.

Enough said - here it is...

Check out PostBIS as well, it already has much more features.

Update to pgchem::tigress isotope pattern generation code

The isotope_pattern() function now contains data for the stable isotopes of 82 elements.

Thus, it fully supports HMDB, UNPD and ChEBI (except the transuranics) and is available here.

The individually affected files are obwrapper.cpp and libmercury++.h, in case you want to update your installation in-place.

The competition catches up - slowly...

http://blogs.msdn.com/b/jocapc/archive/2015/05/16/json-support-in-sql-server-2016.aspx

The Long Tail - vertical table partitioning III

"In part III I'll try to give a raw estimate how big the performance penalty is when the partitioned table switches from fast to slow storage during a query. And there is another important problem to be solved..."

It took some time...

Since I don't have a SMR HDD, I used a standard 5400 RPM 2TB HDD together with a 128 GB SSD instead. Both drives are attached to SATA2 ports.

The test machine is a Fujitsu Workstation with one Intel i7-4770, 16 GB RAM, running Windows 8.1 64 Bit and PostgreSQL 9.4.1 64 Bit.

CrystalDiskMark gives the following performance data for the pair:

HDD:

           Sequential Read :   139.764 MB/s
          Sequential Write :   128.897 MB/s
         Random Read 512KB :    17.136 MB/s
        Random Write 512KB :    71.074 MB/s
    Random Read 4KB (QD=1) :     0.280 MB/s [    68.3 IOPS]
   Random Write 4KB (QD=1) :     0.642 MB/s [   156.8 IOPS]
   Random Read 4KB (QD=32) :     0.999 MB/s [   243.8 IOPS]
  Random Write 4KB (QD=32) :     0.889 MB/s [   217.0 IOPS]

SSD:

           Sequential Read :   431.087 MB/s
          Sequential Write :   299.641 MB/s
         Random Read 512KB :   268.955 MB/s
        Random Write 512KB :   293.199 MB/s
    Random Read 4KB (QD=1) :    24.519 MB/s [  5986.0 IOPS]
   Random Write 4KB (QD=1) :    67.369 MB/s [ 16447.6 IOPS]
   Random Read 4KB (QD=32) :   328.456 MB/s [ 80189.5 IOPS]
  Random Write 4KB (QD=32) :   205.667 MB/s [ 50211.6 IOPS]

As you can see, the SSD is about 3x faster reading sequentially and about 85x - 328x faster reading random blocks, depending on the command queue depth.

PostgreSQL is running with

shared_buffers = 128kB # min 128kB

to minimize cache hits since I want to see how the disks perform.

For the 'benchmark' I first set up two tablespaces, hdd and ssd. Then the long tailed table was created as shown in the previous posts:

CREATE UNLOGGED TABLE fast
(
  id serial NOT NULL,
  value real
)
WITH (
  OIDS=FALSE
)
TABLESPACE ssd;

CREATE UNLOGGED TABLE slow
(
)
INHERITS (fast)
TABLESPACE hdd;

Then I created one billion rows in fast and slow:

INSERT INTO fast (value) SELECT random()*1000000000 FROM generate_series(1,1000000000);

INSERT INTO slow SELECT * FROM fast;

First, I wanted to see how each table performs with full table scans. All these numbers are ten-run averages, that's one reason why it took some time :-)

SELECT avg(value) FROM ONLY slow; -- 210 sec

SELECT avg(value) FROM ONLY fast; -- 90 sec

Which pretty much reflects the 3/1 ratio from CrystalDiskMark.

For the random read test, I created primary keys on the id columns of each table, but put their underlying indexes on the SSD to be fair. Then, 10000 random rows where selected from the whole table:

SELECT avg(value) FROM ONLY fast WHERE id IN (SELECT 1+floor(random()*1000000000)::integer FROM generate_series(1,10000)); -- 6 sec

SELECT avg(value) FROM ONLY slow WHERE id IN (SELECT 1+floor(random()*1000000000)::integer FROM generate_series(1,10000)); -- 100 sec

Here, the HDD is about 16x slower than the SSD.

And from the top 20% of each table:

SELECT avg(value) FROM ONLY fast WHERE id IN (SELECT 800000001+floor(random()*200000000)::integer FROM generate_series(1,10000)); -- 5 sec


SELECT avg(value) FROM ONLY slow WHERE id IN (SELECT 800000001+floor(random()*200000000)::integer FROM generate_series(1,10000)); -- 80 sec

Again, the HDD is about 16x slower than the SSD.

Knowing how each table performs, I then moved the top 20% of rows into fast and left the remaining 80% in slow, thus creating the long tailed table.

SELECT avg(value) FROM fast; -- 178 sec

Surprise, surprise, 210*0.8=168, 90*0.2=18, 168+18=186. The long tailed table is not slower than it's individual parts! 

And with random reads?

Whole table:

SELECT avg(value) FROM fast WHERE id IN (SELECT 1+floor(random()*1000000000)::integer FROM generate_series(1,10000)); -- 50 sec

It's way faster than the table on the SSD alone. This seems to be an anomaly I cannot explain at the moment. Either it helps a lot to have two indexes instead of one, or the most rows where selected from the SSD part.

Top 20% only:

SELECT avg(value) FROM fast WHERE id IN (SELECT 800000001+floor(random()*200000000)::integer FROM generate_series(1,10000)); -- 4 sec

A bit faster than having the whole table on SSD.

Conclusion:

Aside from the (positive) anomaly with random reads on the whole long tailed table, using a long tailed table is at least not slower than a vanilla table but you can put your data graveyard on slow but inexpensive storage while having the hot rows and the indexes on the fast drives. Completely transparent for the clients.

However, one question remains...

Is it possible to ask PostgreSQL what the most frequently accessed rows of a table are?

If so, the background worker could balance the long tailed table without having to know a specific, application dependent access pattern!

An that would be the icing on the cake...

A quick glance over pg_stat* and pg_statio* didn't show anything usable for this task, but I'm open for suggestions. :-)

The Long Tail - vertical table partitioning II

Having all parts from the previous post in place, some mechanism to do the routine maintenance by calling the transfer function automagically, is needed.

Of course this could be done with pgAgent or it could be done with cron, but since it should be elegant, this calls for a background worker process.

For illustration purposes, I wrote a sample implementation called  worker_ltt based on the worker_spi sample code. Sloppy - even the orginal comments are still in there.

Adding worker_ltt to shared_preload_libraries and

worker_ltt.naptime = 60

worker_ltt.database = 'yourdb'

worker_ltt.user = 'youruser'

worker_ltt.function = 'move_longtail'

to postgresql.conf starts executing move_longtail() every 60 seconds in yourdb as youruser. If the user is omitted, it runs with superuser rights!

Since move_longtail() basically can do anything, restricting the user is a good idea.

For more security, the SQL statements could be moved entirely into the background worker, but then the approach loses much of its flexibility... But this is a concept anyway, there is always room for improvement.

But it really works.

In part III I'll try to give a raw estimate how big the performance penalty is when the partitioned table switches from fast to slow storage during a query. And there is another important problem to be solved...

The Long Tail - vertical table partitioning I

DISCLAIMER: This is just an idea, I don't have tried this in a production environment!

Having said that, any input is welcome. :-)

Storing tons of rows in a table of which only a small percentage of rows are frequently queried is a common scenario for RDBMS, especially with databases that have to keep historical information just in case they might be audited, e.g. in environments regulated by law.

With the advent of the first affordable 8TB harddisk and fast SSDs still being much more expensive per GB, I wondered if such long-tailed tables could be split over a SSD holding the frequently accessed pages and a near-line storage HDD keeping the archive - elegantly - with PostgreSQL.

With elegant, I mean without fiddling around with VIEWs, INSTEAD OF triggers and exposing a clean and familiar interface to the developer.

OK, since PostgreSQL already supports horizontal partitioning, spreading one table transparently over many parallel tables, how about vertical partitioning, spreading one table over a hierarchy of speed?

The speed zones can be mapped to tablespaces:

CREATE TABLESPACE fast LOCATION '/mnt/fastdisk';
CREATE TABLESPACE slow LOCATION '/mnt/slowdisk';

Next comes the table(s):

CREATE TABLE the_table
(
  id integer NOT NULL,
  value real
)
WITH (
  OIDS=FALSE
)
TABLESPACE fast;

CREATE TABLE the_table_archive
(
)
INHERITS (the_table)
WITH (
  OIDS=FALSE
)
TABLESPACE slow;

Table inheritance in PostgreSQL is so cool...

And a function to move data from fast to slow:

CREATE OR REPLACE FUNCTION move_longtail()
  RETURNS boolean AS
$BODY$
DECLARE worked BOOLEAN;
DECLARE rowcount INTEGER;
BEGIN
worked := false;
rowcount := count(*) FROM ONLY the_table WHERE id >= 5000000;
IF (rowcount > 100000) THEN
INSERT INTO the_table_archive SELECT * FROM ONLY the_table WHERE id >= 5000000;
DELETE FROM ONLY the_table WHERE id >= 5000000;
worked := true;
END IF;
RETURN worked;
END;
$BODY$
  LANGUAGE plpgsql VOLATILE STRICT;

This function runs only if a minimum of movable rows qualify. This is recommended since SMR disks like large contiguous writes due to how SMR technically works.

Notice the ONLY keyword. This allows to control precisely which partition of the table is affected by the DML statement. Did I say already that table inheritance in PostgreSQL is so cool?

And basically that's it. All the developer sees is SELECT * FROM the _table; or something, being oblivious to the underlying machinery.

Some kind of automatic maintenance is missing. On to part II...

pgchem::tigress 3.2 released

pgchem::tigress 3.2 is finally out!

• This builds against PostgreSQL 9.4.x and OpenBabel 2.3.2 on Linux.
• It contains all fixes and contributions of the previous versions.
• Windows is not supported anymore - and since it builds and runs way better on Linux, probably never will be again.
• Depiction functions have been removed. Their run time was too unpredictable to be run inside a database server.
• Theoretical isotope pattern generation with MERCURY7 is now available with isotopes for 39 elements.

So: CREATE EXTENSION pgchem_tigress;

Counting rows again - inheritance strangeness solved

Taking a second look at the execution plans, I've noticed that the scan on slow read twice the number of pages from disk than the one on fast:

Buffers: shared read=442478 vs. Buffers: shared read=221239

Since I loaded all rows into slow first and then moved 50% of them into fast, this makes sense, I guess.
If I understand it correctly, those pages in slow are now empty, but PostgreSQL keeps them for future use.

So I tried a VACUUM FULL on slow and ran my queries again. That changed the plans:

Buffers: shared read=221239 vs. Buffers: shared read=221239

And execution times are now about equal. The observed effect had nothing to do with table inheritance at all.

So VACUUM FULL can help, when moving big chunks of data between tables with otherwise low write activity.

Counting rows again - inheritance strangeness

Hm, for something I'm trying at the moment on 9.4, I created two identical tables where the second one inherits all from the first:

CREATE UNLOGGED TABLE everything.slow
(
  id integer NOT NULL,
  value real
)
WITH (
  OIDS=FALSE
)

CREATE UNLOGGED TABLE everything.fast
(
)
INHERITS (everything.slow)
WITH (
  OIDS=FALSE
)

No indexes. Default tablespace.

If I then load 50 million records into each of those tables and query them individually using the ONLY restrictor, a count(*) on the parent table (slow) is slower than on the descendant (fast):

select count(*) from only everything.slow;

"Aggregate  (cost=1067783.10..1067783.11 rows=1 width=0) (actual time=4973.812..4973.813 rows=1 loops=1)"
"  Output: count(*)"
"  Buffers: shared read=442478"
"  ->  Seq Scan on everything.slow  (cost=0.00..942722.08 rows=50024408 width=0) (actual time=1012.708..3416.349 rows=50000000 loops=1)"
"        Output: id, value"
"        Buffers: shared read=442478"
"Planning time: 0.118 ms"
"Execution time: 4973.901 ms"

select count(*) from only everything.fast;

"Aggregate  (cost=846239.00..846239.01 rows=1 width=0) (actual time=3988.235..3988.235 rows=1 loops=1)"
"  Output: count(*)"
"  Buffers: shared read=221239"
"  ->  Seq Scan on everything.fast  (cost=0.00..721239.00 rows=50000000 width=0) (actual time=0.101..2403.813 rows=50000000 loops=1)"
"        Output: id, value"
"        Buffers: shared read=221239"
"Planning time: 0.086 ms"
"Execution time: 3988.302 ms"

This works with other aggregates like avg() too.

I had expected some overhead when querying without ONLY on slow, because of the traversal of the inheritance hierarchy, but not when I restrict the query to a specific table with ONLY...

Can someone explain this? UPDATE: Maybe I found it myself. See next post for my explanation.

Finding mass spectra with PostgreSQL: Indexing

When naively searching for a specific spectral contrast angle, e.g.

select id from spectrum, round(spectral_contrast('[{"m/z":144.1150218,"intensity":0.908209840784744},{"m/z":145.118459841064,"intensity":0.0841924148716925}]'::json, id),2) as spectral_contrast where spectral_contrast = 1.0;

performance is not really stellar on my ~ 270k spectra database:

Execution time: 18695.619 ms for 71 hits.

This is no surprise, since the database has to calculate the spectral contrast angle for all rows and filter each row. How about indexing?

The simplest method I can envision, is to precalculate the number of peaks for each spectrum, index the column and add it to the query. Since the spectral contrast angle requires the number of peaks of the compared spectra to be equal, this is a safe restriction.

select id from spectrum, round(spectral_contrast('[{"m/z":144.1150218,"intensity":0.908209840784744},{"m/z":145.118459841064,"intensity":0.0841924148716925}]'::json, id),2) as spectral_contrast where spectral_contrast = 1.0 and num_peaks = 2;

Execution time: 797.346 ms for 71 hits.

OK, but can we do better? Yes, by using a more selective restriction, like the lower and upper bounds of the m/z values of the spectra, index the column and add it to the query. These can easily be obtained by min(m/z)and max(m/z) and then be stored in a numrange column. Since the spectral contrast angle requires the m/z values of the peaks of the compared spectra to be equal, this again is a safe restriction.

select id from spectrum, round(spectral_contrast('[{"m/z":144.1150218,"intensity":0.908209840784744},{"m/z":145.118459841064,"intensity":0.0841924148716925}]'::json, id),2) as spectral_contrast where spectral_contrast = 1.0 and mzrange = numrange(144.1150218,145.118459841064,'[]');

Execution time: 35.128 ms for 71 hits.

About 534 times faster.

Using a range type instead of storing the lower and upper bound in discrete columns gives more flexibility when querying with other criteria, like the Tanimoto index. Then, PostgreSQL's range operators, like &&  (overlap), might come in handy.

Finding mass spectra with PostgreSQL: Spectra as function arguments

If the spectra are represented as tables/recordets, like in my last posts on the topic, the question arises how to use a table as an argument to a function in PostgreSQL without creating a custom datatype or using temporary tables.

1. As one dimensional array of m/z values followed by the corresponding peak values. The array can then be cut in half and unnested inside the function into a set of records:

select unnest(sdarr[1:1]) as "m/z", unnest(sdarr[2:2]) as intensity from ...;

2. As two dimensional array of m/z values and their corresponding peak values. The partial arrays can then be unnested inside the function into a set of records:

select unnest(tdarray[1:3]) as "m/z", unnest(tdarray[4:6]) as intensity from ...;

3. As JSON of m/z values and their corresponding peak values. The JSON can then be converted inside the function into a set of records:
 
select * from json_to_recordset( '[{"m/z":1.0,"intensity":2.2},{"m/z":3.3,"intensity":4.8}]') as x("m/z" double precision, intensity double precision);

All statements above generate a recordset like:

m/z    intensity
1.0    2.2
3.3    4.8
...    ...

In an application, I'd go with JSON, since it has the most understandable structure for a developer against this API and it does not require fiddling around with array support functions of the driver, e.g. like createArrayOf() in JDBC.

count(*) is faster than count(1) - no more with nine.four

In PostgreSQL 9.4, count(1) speed is equally fast to count(*).

Another reason to upgrade to 9.4.

count(<column>) is still slower if no index is used...

count(*) is faster than count(1) revisited

Whoa, I didn't expect that much resonance to the last post. :-)

"Can you post the `explain analyze` output of both queries? I'm pretty sure this is normal "distribution" caused by other stuff going on in the system"

OK. Since my 'test' was on a VirtualBox VM with a self-compiled PostgreSQL and this might not be a reliable test setup, so I hijacked one of our real PostgreSQL servers:

uname -a
Linux  2.6.32-358.23.2.el6.x86_64 #1 SMP Sat Sep 14 05:32:37 EDT 2013 x86_64 x86_64 x86_64 GNU/Linux

Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz ,16 GB RAM, storage on SAN, ext4.

select version();
"PostgreSQL 9.3.5 on x86_64-unknown-linux-gnu, compiled by gcc (GCC) 4.4.7 20120313 (Red Hat 4.4.7-4), 64-bit"

create table hmr(id serial, value real);

insert into hmr (value) select random()*10000000 from generate_series(1,10000000);

CREATE INDEX idx_id
  ON hmr
  USING btree
  (id);

vacuum analyze hmr;

-----------------------------------------------------------------------

explain analyze select count(1) from hmr;

"Aggregate  (cost=169248.00..169248.01 rows=1 width=0) (actual time=1439.521..1439.521 rows=1 loops=1)"
"  ->  Seq Scan on hmr  (cost=0.00..144248.00 rows=10000000 width=0) (actual time=0.006..642.276 rows=10000000 loops=1)"
"Total runtime: 1439.548 ms"

select count(1) from hmr; --811 msec.

-----------------------------------------------------------------------

explain analyze select count(*) from hmr;

"Aggregate  (cost=169248.00..169248.01 rows=1 width=0) (actual time=1299.034..1299.034 rows=1 loops=1)"
"  ->  Seq Scan on hmr  (cost=0.00..144248.00 rows=10000000 width=0) (actual time=0.006..644.912 rows=10000000 loops=1)"
"Total runtime: 1299.061 ms"

select count(*) from hmr; --670 msec 

-----------------------------------------------------------------------

explain analyze select count(id) from hmr;

"Aggregate  (cost=169248.00..169248.01 rows=1 width=4) (actual time=1576.046..1576.046 rows=1 loops=1)"
"  ->  Seq Scan on hmr  (cost=0.00..144248.00 rows=10000000 width=4) (actual time=0.004..636.076 rows=10000000 loops=1)"
"Total runtime: 1576.069 ms"

select count(id) from hmr; --920 msec

----------------------------------------------------------------------- 

And the winner is: still count(*) with count(1) second and count(id) third.

This could be an explanation for count(1) being slower:

"So count(1) is explicitly passing the 1 in and checking it for being NULL before incrementing a counter while count(*) is simply incrementing the counter."

And count(id) might be slower, because it takes additional time to consider using the index?
Or because it's actually reading data: width=4 vs. width=0?

I'm out of my league here.

count(*) is faster than count(1)

Today I've discovered incidentally, that count(*) is slightly faster than count(1) on PostgreSQL 9.3.5:

create table  hmr(id serial, value real);

insert into hmr (value) select random()*10000000 from generate_series(1,10000000);

select count(1) from hmr; --731 msec

select count(*) from hmr; --661 msec

Used repeatedly, e.g. in a function, this can really add up and make a difference.
In the spectral contrast angle CTE from the previous post, it reduces runtime by two seconds or 24%.

I would have expected the constant to be faster than actually reading the row...

Finding mass spectra with PostgreSQL: Spectral contrast angle in SQL

The spectral contrast angle of two spectra is another, according to literature, one of the methods for comparing spectra by similarity.

The spectral contrast angle S is calculated by building a intensity vector in N-dimensional space for each spectrum, where N is the number of m/z peaks in the spectrum, and finding the cosine of the angle between the two vectors. If both are the same, S is 1.0 if the two are orthogonal, S is 0.0.

The formula is S = sum(a.intensity*b.intensity) / sqrt(sum(a.intensity^2)*sum(b.intensity^2)) with a and b being the spectra to compare.

On a table like

CREATE TABLE <spectra_table>
(
  id integer NOT NULL,
  "m/z" numeric NOT NULL,
  intensity numeric NOT NULL
)

this can easily be done in PostgtreSQL with a common table expression:

WITH SQ AS (select "m/z", intensity from <spectra_table> where id = 1 order by "m/z" ASC), ST AS (select  "m/z", intensity from <spectra_table> where id = 2 order by "m/z" ASC),
qc as (select count(1) as qp from sq), tc as (select count(1) as tp from st)
select sum(sq.intensity*st.intensity)/sqrt(sum(sq.intensity^2)*sum(st.intensity^2)) as spectral_contrast from SQ,ST,qc,tc where qc.qp = tc.tp and SQ."m/z" = ST."m/z"

One interesting property of S is, that it evaluates to 1.0 if the intensities of the two spectra are different, but only by a multiple of an integer. If I understood it right, this is correct, because this means that you have measured the same composition at different concentrations.

Put into a function:

CREATE OR REPLACE FUNCTION spectral_contrast(int, int) RETURNS numeric
    AS ' WITH SQ AS (select "m/z", intensity from <spectra_table> where id = $1 order by "m/z" asc), ST AS (select  "m/z", intensity from <spectra_table> where id = $2 order by "m/z" asc),
qc as (select count(1) as qp from sq), tc as (select count(1) as tp from st)
select coalesce(sum(sq.intensity*st.intensity)/sqrt(sum(sq.intensity^2)*sum(st.intensity^2)) ,0.0) as spectral_contrast from SQ,ST,qc,tc where qc.qp = tc.tp and SQ."m/z" = ST."m/z"'
LANGUAGE SQL
    IMMUTABLE
    RETURNS NULL ON NULL INPUT;

Please not the additional coalesce() to return 0.0 when the SQL evaluates to NULL because of the join.

In a real application, the calculation of S can of course be accelerated, e.g. by prefiltering spectra by their number of peaks or their m/z range.

If you want to see the angle in degrees, acosd() is your friend.

That's it for today.

Finding mass spectra with PostgreSQL: Tanimoto similarity in SQL

Now that we can store mass spectra in PostgreSQL, the next question is how to find them?

There is more than one solution to this problem. One is the Tanimoto coefficient or Jaccard index of the peaks.

The Tanimoto coefficient T can be calculated as T = c / (a+b-c) with c the number of peaks both spectra have in common and a and b the number of peaks in each spectrum.

Similarity is then expressed as a number between 0 and 1, with 0 meaning no and 1 maximum similarity. Likewise the Tanimoto distance 1-T expresses the distance between two spectra.

On a table like

CREATE TABLE <spectra_table>
(
  id integer NOT NULL,
  "m/z" numeric NOT NULL,
  intensity numeric NOT NULL
)

this can easily be done in SQL with a common table expression:

WITH SQ AS (select "m/z" from <spectra_table> where id=1),
ST AS (select "m/z" from <spectra_table> where id=2),
SC AS (select count(1)::float as common from SQ, ST where sq."m/z" = st."m/z")
select (select common FROM SC) / ((select count(1) from sq) + (select count(1) from st) - (SELECT common FROM SC))
as spectral_tanimoto;

If desired, further properties like the peak intensity can be used to narrow the match:

WITH SQ AS (select "m/z", intensity from <spectra_table> where id=1),
ST AS (select "m/z", intensity from <spectra_table> where id=2),
SC AS (select count(1)::float as common from SQ, ST where sq."m/z" = st."m/z" and sq.intensity = st.intensity)
select (select common FROM SC) / ((select count(1) from sq) + (select count(1) from st) - (SELECT common FROM SC))
as spectral_tanimoto;

The problem here is, that the join between the peaks relies on exact matching properties. 'Fuzzy' joins can relax this to some extent. The simple variant is to cast the m/z values to integers:


WITH SQ AS (select "m/z"::int from <spectra_table> where id=1),
ST AS (select "m/z"::int from <spectra_table> where id=2),
SC AS (select count(1)::float as common from SQ, ST where sq."m/z" = st."m/z")
select (select common FROM SC) / ((select count(1) from sq) + (select count(1) from st) - (SELECT common FROM SC))
as spectral_tanimoto;

PostgreSQL's range types allow finer grained control:

WITH SQ AS (select "m/z" from <spectra_table> where id=1),
ST AS (select "m/z" from <spectra_table> where id=2),
SC AS (select count(1)::float as common from SQ, ST where numrange(sq."m/z"-0.25, sq."m/z"+0.25,'[]') && numrange(st."m/z"-0.25, st."m/z"+0.25,'[]'))
select (select common FROM SC) / ((select count(1) from sq) + (select count(1) from st) - (SELECT common FROM SC))
as spectral_tanimoto;

Neat. But this comes with a serious caveat, since 'fuzzy' joins may produce excess tuples by their very nature. Given a table with two entries 1.1 and 0.9, joining with 1.1 and a range of +/- 0.2, now both tuples will match, generating two result tuples!

In the context of the Tanimoto coefficient, this can lead to the anomaly that the numerator c becomes larger than the denominator. If this happens, T > 1 or T < 0 may occur, which are strictly undefined. So handle with care.

The nicer properties of the calculation are, that you can easily transform spectra measured with different ionization levels and that it can be made run very fast, if you know the target T you like to filter with beforehand.

1.) Since an m/z of 50 measured at an ionization of +1 means a mass of 50 u but a m/z of 50 measured at ionization +2 means a mass of 100 u (m/z is the ratio between mass and charge of the Ion measured), the statements above would give false results if the ionization was different and not taken into account. This can be done at the join level:

...where sq."m/z"*2 = st."m/z"...
  
Usually the MS software does that for you so you have normalized spectra, but anyway...

2.) If you know the number of peaks in each spectrum and the target T you want to filter with, similarity searches can be made very fast. Here is the paper on how to do it.

That's it for today. Spectral contrast in SQL next.

oracle_fdw: 'cannot use LDAP' means 'don't even touch'

The README for the oracle_fdw clearly says: "You cannot use LDAP functionality both in PostgreSQL and in Oracle, period."

Actually this means: "DO NOT EVEN TOUCH!"

I found this out yesterday when tracking down frequent sigsevs in one of our PostgreSQL servers.

The Oracle instant client was set up with NAMES.DIRECTORY_PATH=(TNSNAMES, LDAP) in sqlnet.ora.

Even when the connect string used in the FDW is in the //<server>:<port>/<service> form, i.e. no Oracle name resolution is required, just allowing LDAP in sqlnet.ora will kill your server.

Calculating theoretical isotope patterns for mass spectrometry with PostgreSQL and pgchem::tigress

New year, new challenge: mass spectra.

To calculate the theoretical isotope pattern for a given molecule in PostgreSQL you need two things:

1. a cheminformatics toolkit to determine the element composition of the molecule and it's exact mass
2. a usably fast and accurate algorithm to infer the isotope pattern from the composition

For 1. I take OpenBabel, for 2. the Mercury7 algorithm as implemented in libmercury++.

Then, the two have to be made working together. Like so:

 

typedef struct
{
    size_t num_entries;
    double *mz;
    double *intensity;
    double *intensity_normalized;
    unsigned int *md;
} _ISOTOPE_PATTERN;

 

extern "C" _ISOTOPE_PATTERN *
ob_molfile_to_isotope_pattern(char *molfile, int charge, double normal)
{
    _ISOTOPE_PATTERN *retval;
    vector msa_mz;
    vector msa_abundance;
    vector composition;
    vector m;
    const double limit = 10e-30;
    OBMol mol;
    OBConversion conv;
    string tmpStr (molfile);
    istringstream molstream (tmpStr);
    double mass_diff, monoisotopic_mass, scale = 1.0, delta = numeric_limits::max();
    int i=0;

    //Initialize composition vector to all zeroes
    for(unsigned int i=0; i         composition.push_back(0);

    conv.SetInAndOutFormats("MDL",NULL);

    conv.Read(&mol, &molstream);

    if (mol.Empty ())
        return NULL;

    // Add missing hydrogens, if any
    mol.AddHydrogens(false, false); // All Hs, no PH correction

    //Iterate over atoms and update the composition vector accordingly
    FOR_ATOMS_OF_MOL(atom, mol)
    {
        switch (atom->GetAtomicNum())
        {
        case 1: // H
            composition[0]++;
            break;
        case 6: // C
            composition[1]++;
            break;
        case 7: // N
            composition[2]++;
            break;
        case 8: // O
            composition[3]++;
            break;
        case 16: // S
            composition[4]++;
            break;
        case 35: // Br
            composition[5]++;
            break;
        case 17: // Cl
            composition[6]++;
            break;
        case 53: // I
            composition[7]++;
            break;
        case 15: // P
            composition[8]++;
            break;
        case 9: // F
            composition[9]++;
            break;
        default: // Others ignored
            break;
        }
    }

    // Calculate isotope patterns with MERCURY7
    if(0 != mercury::mercury(msa_mz, msa_abundance, composition, charge, limit))
    {
        return NULL;
    }

    monoisotopic_mass = mol.GetExactMass();

    // Allocate return value and copy values
    retval = (_ISOTOPE_PATTERN*) calloc(1,sizeof(_ISOTOPE_PATTERN));

    retval->num_entries = msa_mz.size();
    retval->mz=(double*) calloc(msa_mz.size(), sizeof(double));
    retval->intensity=(double*) calloc(msa_abundance.size(), sizeof(double));
    retval->intensity_normalized=(double*) calloc(msa_abundance.size(), sizeof(double));
    retval->md=(unsigned int*) calloc(msa_abundance.size(), sizeof(unsigned int));

    copy(msa_mz.begin(), msa_mz.end(), retval->mz);
    copy(msa_abundance.begin(), msa_abundance.end(), retval->intensity);
    copy(msa_abundance.begin(), msa_abundance.end(), retval->intensity_normalized);

    for(std::vector::iterator it = msa_mz.begin(); it != msa_mz.end(); it++)
    {
        mass_diff = *it-monoisotopic_mass;

        m.push_back(mass_diff);

        if(abs(mass_diff) < delta)
        {
            delta = abs(mass_diff);
            scale = msa_abundance[i];
        }
        i++;
    }

    scale = normal / scale;

    for(i=0; retval->num_entries; i++)
    {
        retval->intensity_normalized[i] *= scale;
    }

    copy(m.begin(), m.end(), retval->md);

    return retval;
}
 

This parses a V2000 or V3000 molfile into a molecule object, iterates over the atoms and generates the elemental composition for Mercury7, calculates the exact mass and the theoretical isotope pattern. It then stores the m/z vector, the peak vector and the derived peak vector normalized to some value, e.g. 100%, and the mass distance relative to the base peak m/z value. All of this is stored in the C structure _ISOTOPE_PATTERN in order to transfer it from C++ to the C code of PostgreSQL.

 

Over to the PostgreSQL side of things :-)

 

PG_FUNCTION_INFO_V1 (pgchem_isotope_pattern);

Datum
pgchem_isotope_pattern (PG_FUNCTION_ARGS)
{
    _ISOTOPE_PATTERN *isotope_pattern = NULL;
    _ISOTOPE_PATTERN *pattern_copy = NULL;
    FuncCallContext *ctx=NULL;
    MemoryContext original_ctx=NULL;
    DECOMPRESSED_DATA *original_data=NULL;
    char *tmpMolfile=NULL;

    if(SRF_IS_FIRSTCALL())
    {
        MOLECULE *arg_molecule = PG_GETARG_MOLECULE_P (0);
        int32 arg_charge = PG_GETARG_INT32 (1);
        float8 arg_normal = PG_GETARG_FLOAT8 (2);
        ctx = SRF_FIRSTCALL_INIT();

        original_ctx = MemoryContextSwitchTo(ctx->multi_call_memory_ctx);

        if(arg_molecule->original_format==FORMAT_V2000 || arg_molecule->original_format==FORMAT_V3000)
        {
            original_data = decompress_data(CIPTR(arg_molecule),arg_molecule->compressed_sizeo, arg_molecule->sizeo);
            tmpMolfile = strdup(original_data->decompressed_data);
        }
        else
        {
            tmpMolfile = ob_smiles_to_V2000 (SMIPTR(arg_molecule));
        }

        isotope_pattern = ob_molfile_to_isotope_pattern(tmpMolfile, arg_charge, arg_normal);

        if(NULL!=tmpMolfile)
        {
            free(tmpMolfile);
        }

        if(NULL == isotope_pattern)
        {
            elog (ERROR, "Could not generate isotope pattern!");
            PG_RETURN_NULL();
        }

        pattern_copy = (_ISOTOPE_PATTERN*) palloc0(sizeof(_ISOTOPE_PATTERN));
        pattern_copy->mz = (double*) palloc0(isotope_pattern->num_entries*sizeof(double));
        pattern_copy->intensity = (double*) palloc0(isotope_pattern->num_entries*sizeof(double));
        pattern_copy->intensity_normalized = (double*) palloc0(isotope_pattern->num_entries*sizeof(double));
        pattern_copy->md = (unsigned int*) palloc0(isotope_pattern->num_entries*sizeof(unsigned int));

        pattern_copy->num_entries = isotope_pattern->num_entries;

        memcpy(pattern_copy->mz, isotope_pattern->mz, isotope_pattern->num_entries*sizeof(double));
        memcpy(pattern_copy->intensity, isotope_pattern->intensity, isotope_pattern->num_entries*sizeof(double));
        memcpy(pattern_copy->intensity_normalized, isotope_pattern->intensity_normalized, isotope_pattern->num_entries*sizeof(double));
        memcpy(pattern_copy->md, isotope_pattern->md, isotope_pattern->num_entries*sizeof(unsigned int));

        if(NULL != isotope_pattern->md)
            free(isotope_pattern->md);

        if(NULL != isotope_pattern->mz)
            free(isotope_pattern->mz);

        if(NULL != isotope_pattern->intensity)
            free(isotope_pattern->intensity);

        if(NULL != isotope_pattern->intensity_normalized)
            free(isotope_pattern->intensity_normalized);

        if(NULL != isotope_pattern)
            free(isotope_pattern);

        isotope_pattern = NULL;

        ctx->max_calls = pattern_copy->num_entries;
        ctx->user_fctx = pattern_copy;

        if(get_call_result_type(fcinfo, NULL, &ctx->tuple_desc) != TYPEFUNC_COMPOSITE)
        {
            elog (ERROR, "Calling the isotope pattern SRF in a context that does not expect tuples in return is not supported!");
        }

        BlessTupleDesc(ctx->tuple_desc);

        MemoryContextSwitchTo(original_ctx);
    }

    ctx = SRF_PERCALL_SETUP();

    isotope_pattern = ctx->user_fctx;

    if(ctx->call_cntr < ctx->max_calls)
    {
        HeapTuple rettuple = NULL;
        Datum *retvals;
        bool *retnulls;

        retvals = (Datum*) palloc0(4*sizeof(Datum));
        retnulls = (bool*) palloc0(4*sizeof(bool));

        retvals[0] = Float8GetDatum(isotope_pattern->mz[ctx->call_cntr]);
        retvals[1] = Float8GetDatum(isotope_pattern->intensity[ctx->call_cntr]);
        retvals[2] = Float8GetDatum(isotope_pattern->intensity_normalized[ctx->call_cntr]);
        retvals[3] = UInt8GetDatum(isotope_pattern->md[ctx->call_cntr]);

        retnulls[0] = false;
        retnulls[1] = false;
        retnulls[2] = false;
        retnulls[3] = false;

        rettuple = heap_form_tuple(ctx->tuple_desc, retvals, retnulls);

        pfree(retvals);
        pfree(retnulls);

        SRF_RETURN_NEXT(ctx, HeapTupleGetDatum(rettuple));
    }
    else
    {
        SRF_RETURN_DONE(ctx);
    }
}
 

This is a set returning function, meaning that it can return multiple rows in one call, i.e. it returns a complete table. Basically it retrieves the molecule argument from PostgreSQL, calls ob_molfile_to_isotope_pattern(), constructs a set of tuples and returns them to PostgreSQL. The basics of set returning functions can be found here and here.

 

Now we can declare the function to PostgreSQL:

 

CREATE OR REPLACE FUNCTION isotope_pattern(IN molecule, IN integer DEFAULT 0, IN double precision DEFAULT 100.0)
  RETURNS TABLE("m/z" double precision, intensity double precision, intensity_normalized double precision, mdtbp integer) AS
'libpgchem', 'pgchem_isotope_pattern'
  LANGUAGE c IMMUTABLE STRICT;
 

and use it:

select * from isotope_pattern('c1ccccc1I'::molecule) where intensity >= 0.01;
 

m/z	intensity	intensity_normalized	mdtbp
203.9436	0.934689933304048	100	0
204.946987674881	0.0634781699018538	6.79136124612619	1

 

  

Visualized it looks like this:

Nice. And everything (except the GUI) in the database.