deletions | additions       diff --git a/Genome Assembly and Annotation.md b/Genome Assembly and Annotation.md  index 986c2b5..5731630 100644  --- a/Genome Assembly and Annotation.md  +++ b/Genome Assembly and Annotation.md 
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To open A5-miseq stats, import it into Excel as a tab delimited CSV file.  The first two numbers, shown in columns 2 and 3, are the number of contigs and scaffolds. Defining a "good" or "bad" assembly starts here. A finished assembly would consist of a single contig with no unresolved nucleotides but that is extremely unlikely to result from short read data. At the other extreme, we would consider a bacterial assembly in 1000 contigs to be very fragmented. In our experience, acceptable bacterial assemblies using Illumina PE300 data, assembled with A5, tend to range from 10-200 contigs. It is also worth noting that unless studying genomic organization, the number of contigs is less important than the gene content recovered by the assembly which is typically >99% using A5-miseq \cite{25338718}.  "Genome Size" and "Longest Scaffold" are simply represented in base pairs. While genome size can vary within taxa, this can be a second useful sanity check for the assembly. When expecting a 5MB genome based on other sequenced isolates from the same genus, if the assembled genome size is 2MB 2 MB  or 10MB, 10 MB,  a red flag should be raised. "N50" represents the contig size at which at least 50% of the assembly is contained in contigs of that size or larger. This metric, combined with the number of contigs is the most common measure of assembly quality… larger is better. An N50 of 5,000 bp would be quite poor... meaning that half of the entire assembly is in contigs smaller than 5,000 bp. On the other hand an N50 of 1,000,000 bp is considered very good for bacterial genomes sequenced with Illumina technology. The number of raw reads/raw nucleotides "Raw reads"/"Raw nt" and error-corrected reads/nucleotides "EC Reads"/"Raw nt" counts are useful for seeing what percentage of the data has been discarded. A very large difference between these numbers ("% reads passing EC"/"% nt passing EC") would indicate either poor quality sequence data or significant adapter contamination. Adapter contamination rates can be high when the insert size is too small or if there were problems during library preparation. Poor quality sequence data can result from loading the libraries at a molar concentration that was too high for the instrument, from mechanical issues preventing focus of the sequencing instrument's cameras, or from use of a compromised batch of sequencing reagents. Resolution of these issues would entail a discussion with your sequencing provider.