Score calculation in HaploGrep

In this blog post we show how Haplogrep’s default measure (so called Kulczynski measure) works on a concrete example. (Note: this blog post has been updated after receiving a correction from Chris Simpson, Thanks!)

So let’s say this is your input sample in hsd format:

test 16024-16569;1-576; ? 73G 263G 285T 315.1C 455.1T 523G 524T 16051G 16129A 16188.1C 16249C 16264G

The Kulczynski measure is defined as follows:
(HaplogroupWeight + SampleWeight) * 0.5

HaploGrep applies this formula to all haplogroups in Phylotree and finally returns the overall best hit. In this example, I’ll calculate the measure only for the best hit, this is in our case U1a2.

1) First, we have to calculate the HaplogroupWeight:

HaplogroupWeight = FoundPolymorphismsWeight/ExpectedPolymorphismsWeight

Found Polymorphisms: Polymorphisms from the input sample that are detected (or found) in the currently tested haplogroup (i.e. in our case U1a2)

Expected Polymorphisms: Polymorphisms that are included (or expected) in the currently tested haplogroup (i.e. in our case U1a2).

Found polymorphisms + weights:
455.1T (6.7), 263G (8.8), 285T (10.0), 16249C (4.5), 16129A->2.6, 73G (5.6)

Expected polymorphisms + weights:
455.1T (6.7), 263G (8.8), 285T (10.0), 16189C (2.0), 16249C (4.5), 16129A (2.6), 73G (5.6)

6.7 + 8.8 + 10 + 4.5 + 2.6 + 5.6 = 38.2

6.7 + 8.8 + 10 + 2 + 4.5 + 2.6 + 5.6 = 40.2

HaplogroupWeight: 41.5 / 43.5 = 0.9540229885

As you can see only 16189C is not found but expected by the haplogroup.

2) Second, we have to calculate the SampleWeight:

SampleWeight = FoundPolymorphismsWeight/SamplePolymorphismsWeight

Found Polymorphisms: see above

Sample Polymorphisms: Polymorphisms that are included in the sample and are falling into the specified range (2nd column of hsd, in our case 16024-16569;1-576;)

Sample polymorphisms (weights):
16264G (0.0), 16188.1C (0.0), 263G (8.8), 311C (0.0), 16129A (2.6), 16051G (4.5), 455.1T (6.7), 523G (0.0), 285T (10.0), 524T (0.0), 16249C (4.5), 73G (5.6)

8.8 + 2.6 + 4.5 + 6.7 + 10 + 4.5 + 5.6 = 42.7

SampleWeight: 41.5 / 46 = 0.90217391304

As you can see 16051G is included in the sample but not required by the haplogroup.

3) Third use the calculate weights and calculate the final measure:
(HaplogroupWeight + SampleWeight) * 0.5
(0.90217391304 + 0.9540229885) / 2 = 0.92809845077

The best hit (U1a2) has a quality of 0.928. We also integrated that as an automatic test case here.

Keep in mind that in a real life scenario the quality of all haplogroups are calculated, sorted and the best 50 hits are returned.

Hope this helps to understand the measure.


HaploGrep’s RestAPI now available!

Hi all,
due to many requests we are happy to announce┬áthat we provide Haplogrep’s┬áREST API to the public! This will allow everyone to determine haplogroups in a very convenient way. In the following snippets we show how a simple call works (a) from the UNIX command line using curl and (b) from Java. If you have an example running for other languages, please let us know and we will add it here.

Happy haplogrouping & happy new year!

1) Unix Command Line: This call uploads the file myfile.vcf and returns a JSON String including the tags “id” and “haplogroup”:

curl -i -X POST -H "Content-Type: multipart/form-data" -F "importfile=@myfile.vcf"

2) Here you can see the exactly same call with Java:

import org.restlet.resource.*;
import org.restlet.ext.html.*;
import org.restlet.representation.*;
import org.json.*;


public class SampleClient {
public static void main(String[] args) throws IOException {
// change location here
File file = new File("/home/seb/samplefile.hsd");
//POST file
ClientResource cr = new ClientResource("");
final FormDataSet fds = new FormDataSet();
final FormData fileRep = new FormData("importfile", new FileRepresentation(file, MediaType.APPLICATION_ALL));

JSONArray jsonArray = new JSONArray(cr.getResponse().getEntityAsText());
for (int i = 0; i < jsonArray.length(); i++) {
JSONObject object = jsonArray.getJSONObject(i);
String id = (String) object.get("id");
String hg = (String) object.get("haplogroup");
Status status = cr.getResponse().getStatus();


NGS, HaploGrep and Hadoop MapReduce

Many of our HaploGrep users are interested in analysing data within automated pipelines. To support users with a scalable and standardized service, we are happy to present you our newest project, called mtDNA-Server.
mtDNA-Server provides a free service for the complete workflow of NGS mtDNA projects. It includes the alignment of FASTQ SE/PE data based on BWA MEM (using JBWA) , sorting of data, creation of BAM files, heteroplasmy detection, contamination identification, haplogroup assignment using of course HaploGrep and graphical report creation based on R.
All steps are parallelized using Hadoop MapReduce. Therefore, we are able to analyse 800 1000G Phase 1 samples (27 GB) on our 30 core test cluster within 30 minutes. To simplify the execution of MapReduce jobs and provide users an intelligent workflow system, we use our MapReduce framework Cloudgene. Cloudgene controls the complete workflow and provides an intelligent queuing system in the background. mtDNA-Server will be available as a download in near future.

For now, we are still in beta and would very much appreciate any kind of feedback!!
Here’s the link: