fix citations

paper/paper.bib

@@ -1,4 +1,4 @@
-@article{Nayfach_Shi_Seshadri_Pollard_Kyrpides_2019, 
+@article{Nayfach2019global_human_gut_microbiome, 
 title={New insights from uncultivated genomes of the global human gut microbiome}, 
 volume={568}, 
 DOI={10.1038/s41586-019-1058-x}, 

paper/paper.md

@@ -23,9 +23,9 @@ bibliography: paper.bib
 
 
 # Statement of need
-Metagenomics enables the study of microbial communities and their individual members through shotgun sequencing. An essential phase of metagenomic analysis is the recovery of metagenome-assembled genomes (MAGs). MAGs serve as a gateway to additional analyses, including the exploration of organism-specific metabolic pathways, and form the basis for comprehensive large-scale metagenomic surveys [@Nayfach_Shi_Seshadri_Pollard_Kyrpides_2019, @Acinas_Sánchez_et_al_2021]. 
+Metagenomics enables the study of microbial communities and their individual members through shotgun sequencing. An essential phase of metagenomic analysis is the recovery of metagenome-assembled genomes (MAGs). MAGs serve as a gateway to additional analyses, including the exploration of organism-specific metabolic pathways, and form the basis for comprehensive large-scale metagenomic surveys [@Nayfach2019global_human_gut_microbiome] [@Acinas_Sánchez_et_al_2021]. 
 
-In a metagenomic analysis, sequence reads are first assembled into longer sequences called contigs. These contigs are then grouped into bins based on common characteristics  in a process called metagenomic binning to obtain MAGs. There are several tools that can be used to binned contigs into MAGs. These tools are based on various statistical and machine learning methods and use contig characteristics such as tetranucleotide frequencies, GC content and similar abundances across samples [@kang2019metabat, @alneberg2014concoct, @nissen2021improved]. 
+In a metagenomic analysis, sequence reads are first assembled into longer sequences called contigs. These contigs are then grouped into bins based on common characteristics  in a process called metagenomic binning to obtain MAGs. There are several tools that can be used to binned contigs into MAGs. These tools are based on various statistical and machine learning methods and use contig characteristics such as tetranucleotide frequencies, GC content and similar abundances across samples [@kang2019metabat] [@alneberg2014concoct] [@nissen2021improved]. 
 
 The approach of applying multiple binning methods and combining them has proven useful to obtain more and better quality MAGs from metagenomic datasets.This combination process is called bin-refinement and several tools exist to perform such tasks, such as DASTool [@sieber2018dastool], MagScot [@ruhlemann2022magscot] and the bin-refinement module of the metaWRAP pipeline [@uritskiy2018metawrap]. Of these, metaWRAP's bin-refinement tool has demonstrated remarkable efficiency in benchmark analysis [@meyer2022critical]. However, it has certain limitations, most notably its inability to integrate more than three binning results. In addition, it repeatedly uses CheckM  [@parks2015checkm] to assess bin quality throughout its execution, which contributes to its slower performance. Furthermore, since it is embedded in a larger framework, it may present challenges when attempting to integrate it into an independent analysis pipeline.