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Hadoop-BAM is a library of Apache Hadoop map/reduce functions for the analysis and manipulation of BAM files.
The Hadoop map/reduce functions are based on the Picard Java Library and are mapped to the functions provided by the Hadoop library.
Hadoop-BAM is developed by a group of researchers from the University of Gothenburg, Sweden. The initial focus of the library is on the analysis of genomic data, but it is extensible to other similar data.
Hadoop-BAM contains the following components:
o Support for a wide range of BAM file formats.
o Support for the general Hadoop map/reduce framework, without specific BAM support.
o Support for common analysis operations, using the well-known Picard API.
o An experimental statistical framework for processing of grouped BAM files, making use of Apache Spark.
Hadoop-BAM Development Status
The code for Hadoop-BAM is developed on GitHub and is built with the Apache Maven build management system.
Maven supports Java and Scala, so Hadoop-BAM can be built in those languages.
The current version of the library is 0.6.1, which supports Hadoop 1.1.x and offers backward compatibility to 0.5.x.
Hadoop-BAM is under active development; the latest code can be found on GitHub.
For more information about Hadoop-BAM, you can consult the following resources:
[1]
Download
Source distribution packages are available in tar, zip, and war formats from the Apache Incubator repository.
For detailed documentation, see the project documentation wiki:
For binary distributions, go to Apache Incubator:
Documentation
This is the documentation wiki of Hadoop-BAM; we are working on getting it built and published to some kind of project web pages.
Dependencies
Hadoop-BAM requires the Hadoop Common Library, and the Hadoop MapReduce Libraries, but does not depend on them directly.
Currently, Hadoop-B
Hadoop-BAM Crack + With License Key
Based on an existing Java implementation of the SAM SAM-JDK, Hadoop-BAM Product Key provides a high performance mapping and reducing framework for a next generation sequencing data pipeline. It was created with a goal to simplify the integration of Genomic analysis and Hadoop through a set of convenient API (both for Java and Scala users). However, Hadoop-BAM is not limited to Genomic applications and can be easily adopted for any distributed application.
Hadoop-BAM is a proof of concept (POC) intended to demonstrate the feasibility of a fully distributed pipeline that can run on multiple machines and reads.
This PAM instance defines password policies for the local user. In addition, the server provides POSIX accounts that can be used with pam_faillock, pam_unix, pam_shells and pam_access.
For security reasons, it is possible to restrict access to applications using these accounts.
Each line is a list containing parameters, such as the class name and options.
Please see class and options descriptions for more information.
Configuration File
You can use the PAM.ini configuration file, located in the /etc/pam.d directory.
The configuration file lists only PAM instances that are inherited by services. You should list only the PAMs of the services you want to configure.
The configuration file should be located under /etc/pam.d/ with the given name.
For example, if you want to configure PAM for ssh but do not want to configure all the services using PAM, you would use the following configuration file:
This configuration file has the following sample entries:
In addition, you can specify an options.d directory, which has options to define policy.
This configuration file should be located in /etc/pam.d with the given name.
Example Files
To list all available files, use the pam_info command.
root@rapids:~# pam_info -l
NAME
––––
libpam
libpam-auth
libpam-auth-update
libpam-runtime
libpam-fs
libpam-krb5
libpam-ldap
libpam-xauth
libpam-wheel
libpam-ssh
libpam-systemd
libpam
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Hadoop-BAM addresses a simple problem: How to reliably access the BAM files?
Hadoop-BAM answers this by providing a Java interface over BAM files that has the following benefits over the existing BAM access technologies:
Convenience of the existing BAM tools.
Completeness: The library implements all BAM file operations.
Easy aggregation of multiple BAM files.
Note: For the sake of brevity, the term BAM file here refers to a file that contains alignments or paired reads. It does not refer to the traditional representation of a genome-scale assembly.
The shortcomings of the existing APIs are:
Unreliable: Data may get lost because of network failure or because of memory storage failure.
Hard to write reusable component: Since existing APIs have tight coupling with their particular library implementations, they are not easily reusable for other applications.
SparkFun is an online electronic component retailer based in Boulder, Colorado, that offers fresh components, kits, educational products, and repair parts for STEM enthusiasts of all ages. Our customers are kids, parents, hobbyists, DIYers, technologists, engineers, and designers who enjoy learning about electronics and inventing something new. SparkFun is a privately owned subsidiary of M+W Group, LLC and is a member of the Chip Squad.
SparkFun is an online electronic component retailer based in Boulder, Colorado, that offers fresh components, kits, educational products, and repair parts for STEM enthusiasts of all ages. Our customers are kids, parents, hobbyists, DIYers, technologists, engineers, and designers who enjoy learning about electronics and inventing something new. SparkFun is a privately owned subsidiary of M+W Group, LLC and is a member of the Chip Squad.
Modular Trends reports quarterly about the growing modular ecosystem and seeks to give an overview of the state of the industry and how the trends are developing.
Note: We ask to keep comments brief, polite and relevant. Let us know if there is something you’d rather we discuss in the comments section. To get moderation, digg/reddit/etc, on comments, we use several spam filters, so there may be some delay between posts if we or a spam filter catches it.
Modular Trends reports quarterly about the growing modular ecosystem and seeks to give an overview of the state of the industry and how the trends are developing.
Note:
What’s New in the Hadoop-BAM?
Hadoop-BAM was created as an open source, Java-based library for distributed processing of genetic data from next generation sequencer machines.
It allows scalable manipulation of aligned reads in the Hadoop distributed computing framework. It acts as an integration layer between analysis applications and BAM (Binary Alignment/Map) files that are processed using Hadoop.
Hadoop-BAM solves the issues related to BAM data access by presenting a convenient API for implementing map and reduce functions in the Hadoop map-reduce framework.
The library builds on top of the popular Picard SAM-JDK, so tools that rely on the Picard API are expected to be easily convertible to support large scale distributed processing.
This is the final version and should be compatable with new versions of Hadoo.
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