Sources paper.md

Author: keshav-gandhi

paper.md

@@ -21,7 +21,7 @@ authors:
     affiliation: 1
 
 affiliations:
- - name: National Center for Supercomputing Applications, Genomics Group, Urbana, IL, USA, 61801 
+ - name: National Center for Supercomputing Applications, Genomics Group, Urbana, IL, USA, 61801, 
    index: 1
  - name: University of Illinois at Chicago, Chicago, IL, USA, 60607
    index: 2
@@ -37,7 +37,7 @@ While the field of genomics has advanced significantly with the advent of high-t
 
 # Statement of need
 
-The NExt-generation sequencing Analysis Toolkit (NEAT) is an open-source Python package that creates simulated next-generation sequencing datasets. NEAT’s simulations account for a wide range of sequencing parameters (e.g., DNA read fragment length, sequencing error rates, mutation frequencies, etc.) and allow users to customize their sequencing data.<sup>1</sup> Since the original release of NEAT in 2016, most scripts have been greatly modified, and NEAT is currently on version 4.2. The code has undergone significant ongoing changes since 2020. Upgrading to Python 3 has enabled NEAT to achieve a flexible and intuitive user interface with minimal dependencies. The toolkit is optimized for both speed and accuracy, and new features have been implemented, such as improved ploidy simulation, mutation modeling, and the ability to model mutational profiles directly from data. A summary of algorithmic changes is provided in Table 1.
+The NExt-generation sequencing Analysis Toolkit (NEAT) is an open-source Python package that creates simulated next-generation sequencing datasets. NEAT’s simulations account for a wide range of sequencing parameters (e.g., DNA read fragment length, sequencing error rates, mutation frequencies, etc.) and allow users to customize their sequencing data.[@Stephens:2016] Since the original release of NEAT in 2016, most scripts have been greatly modified, and NEAT is currently on version 4.2. The code has undergone significant ongoing changes since 2020. Upgrading to Python 3 has enabled NEAT to achieve a flexible and intuitive user interface with minimal dependencies. The toolkit is optimized for both speed and accuracy, and new features have been implemented, such as improved ploidy simulation, mutation modeling, and the ability to model mutational profiles directly from data. A summary of algorithmic changes is provided in Table 1.
 
 NEAT can integrate seamlessly with existing bioinformatics workflows, providing outputs in several common file formats. The toolkit’s ability to simulate gold-standard synthetic datasets with ground truth annotations is useful for testing bioinformatics pipelines. Uses of NEAT continue to be prominently featured—from scientists who have comprehensively sequenced the human Y chromosome<sup>2</sup> to researchers who use NEAT to evaluate and validate the performance of other high-profile bioinformatics tools.<sup>3, 4</sup> Earlier versions of NEAT have also demonstrated utility when benchmarked in comparison to similar tools.<sup>5</sup> The source code for both original and updated versions of NEAT is freely available on GitHub.<sup>1</sup>