Name: PRACE Seasonal School on Bioinformatics
Start: 2018-04-23T08:00:00+02:00
End: 2018-04-26T18:00:00+02:00
Location: SAS campus

PRACE Seasonal School on Bioinformatics

from Monday, April 23, 2018 (8:00 AM) to Thursday, April 26, 2018 (6:00 PM)

Monday, April 23, 2018
9:00 AM Opening - Lukáš Demovič (Computing Center of the SAS)
Opening
- Lukáš Demovič (Computing Center of the SAS)
9:00 AM - 9:30 AM
9:30 AM Introduction to bioinformatics 1/2 - Erik Bongcam Rudloff (Swedish University of Agricultural Sciences)
Introduction to bioinformatics 1/2
- Erik Bongcam Rudloff (Swedish University of Agricultural Sciences)
9:30 AM - 10:30 AM
10:30 AM Coffee break
Coffee break
10:30 AM - 11:00 AM
11:00 AM Introduction to bioinformatics 2/2 - Erik Bongcam Rudloff (Swedish University of Agricultural Sciences)
Introduction to bioinformatics 2/2
- Erik Bongcam Rudloff (Swedish University of Agricultural Sciences)
11:00 AM - 12:00 PM
12:00 PM Lunch
Lunch
12:00 PM - 1:30 PM
1:30 PM Parallel programming with R (1/2) - Sirkiä Seija (Centre fo Scientific Computing - CSC, Finland)
Parallel programming with R (1/2)
- Sirkiä Seija (Centre fo Scientific Computing - CSC, Finland)
1:30 PM - 3:15 PM
This lecture is aimed at R users with very limited or no experience in parallel computing. You will learn how and when taking advantage of parallel computing can help you run your R scripts in less time, when not, and how to tell the difference. More importantly, you will get an idea of how to approach parallelising your task in practice. We will consider Intel Math kernel library (MKL) together with Microsoft R Open, and R packages snow and foreach, both used as backend by various Bioconductor and CRAN packages. Lecture will include live coding demos. Prerequisites: experience in using R for data analysis in research
3:15 PM Coffee break
Coffee break
3:15 PM - 3:45 PM
3:45 PM Parallel programming with R (2/2) - Sirkiä Seija (Centre fo Scientific Computing - CSC, Finland)
Parallel programming with R (2/2)
- Sirkiä Seija (Centre fo Scientific Computing - CSC, Finland)
3:45 PM - 5:30 PM
This lecture is aimed at R users with very limited or no experience in parallel computing. You will learn how and when taking advantage of parallel computing can help you run your R scripts in less time, when not, and how to tell the difference. More importantly, you will get an idea of how to approach parallelising your task in practice. We will consider Intel Math kernel library (MKL) together with Microsoft R Open, and R packages snow and foreach, both used as backend by various Bioconductor and CRAN packages. Lecture will include live coding demos. Prerequisites: experience in using R for data analysis in research
Tuesday, April 24, 2018
9:00 AM Analysing large datasets with Apache Spark (1/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (1/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
9:00 AM - 10:30 AM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
10:30 AM Coffee break
Coffee break
10:30 AM - 11:00 AM
11:00 AM Analysing large datasets with Apache Spark (2/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (2/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
11:00 AM - 12:30 PM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
2:00 PM Analysing large datasets with Apache Spark (3/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (3/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
2:00 PM - 3:30 PM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
3:30 PM Coffee break
Coffee break
3:30 PM - 4:00 PM
4:00 PM Analysing large datasets with Apache Spark (4/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (4/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
4:00 PM - 5:30 PM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
7:00 PM

7:00 PM - 9:30 PM
Wednesday, April 25, 2018
9:00 AM Analysing large datasets with Apache Spark (5/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (5/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
9:00 AM - 10:30 AM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
10:30 AM Coffee break
Coffee break
10:30 AM - 11:00 AM
11:00 AM Analysing large datasets with Apache Spark (6/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (6/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
11:00 AM - 12:30 PM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
2:00 PM Analysing large datasets with Apache Spark (7/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (7/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
2:00 PM - 3:30 PM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
3:30 PM Coffee break
Coffee break
3:30 PM - 4:00 PM
4:00 PM Analysing large datasets with Apache Spark (8/8) - Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
Analysing large datasets with Apache Spark (8/8)
- Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
4:00 PM - 5:30 PM
With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing for big data based computing, why should you use Spark for big data analysis and why should you care about running Spark on cloud. Next, the technical architechture and use cases of Spark, some ways to set it up, best practices and programming aspects. The first day includes the overview, architectural concepts, programming with Spark's fundamental data structure (RDD) and basics of Machine Learning with Spark. The second day focuses on the SQL module of Spark, which allows the user to analyse data using Spark's distributed collection (Dataframes) by using the traditional SQL queries, best practices when using spark, demo of a working Spark cluster, using Spark Streaming over a live twitter data. Spark can be an ideal platform for bioinformatics when it comes to building analysis pipelines and workflows. Spark supports languages such as R, Python, and SQL which eases the learning for practicing bioinformaticians. Spark is constantly growing with new libraries for bioinformatics analysis, although widespread usage will take sometime because the traditional methods need some rewriting in Spark. But, with the community constantly evolving, it is good chance to learn Spark and implement your own methods in it, for doing large scale data analysis.
Thursday, April 26, 2018
9:00 AM BLAST (1/2) - Kimmo Mattila (Centre fo Scientific Computing - CSC, Finland)
BLAST (1/2)
- Kimmo Mattila (Centre fo Scientific Computing - CSC, Finland)
9:00 AM - 10:30 AM
Running BLAST in Clusters NCBI BLAST is one of the most of the most frequently used bioinformatics tools. BLAST answers to the question: “What known sequences are significantly similar to my sample sequence”. Answers to this question is needed in numerous bioinformatics analyses and work-flows. As the sequence databases keep growing as well as the sizes of the data sets to be analyzed, a HPC cluster environment is often needed for BLAST analyses. In this half a day session we briefly go though the basic features of BLAST and issues related to maintaining and using BLAST in HPC cluster environments.
10:30 AM Coffee break
Coffee break
10:30 AM - 11:00 AM
11:00 AM BLAST (2/2) - Kimmo Mattila (Centre fo Scientific Computing - CSC, Finland)
BLAST (2/2)
- Kimmo Mattila (Centre fo Scientific Computing - CSC, Finland)
11:00 AM - 12:40 PM
Running BLAST in Clusters NCBI BLAST is one of the most of the most frequently used bioinformatics tools. BLAST answers to the question: “What known sequences are significantly similar to my sample sequence”. Answers to this question is needed in numerous bioinformatics analyses and work-flows. As the sequence databases keep growing as well as the sizes of the data sets to be analyzed, a HPC cluster environment is often needed for BLAST analyses. In this half a day session we briefly go though the basic features of BLAST and issues related to maintaining and using BLAST in HPC cluster environments.
12:40 PM Closing remarks - Lukáš Demovič (Computing Center of the SAS)
Closing remarks
- Lukáš Demovič (Computing Center of the SAS)
12:40 PM - 1:00 PM
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM

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