add logo to readme

README.md

@@ -1,5 +1,7 @@
 # pggb
 
+![PanGenome Graph Builder](https://raw.githubusercontent.com/pangenome/pggb/master/data/images/pggb-logo-rounded.png)
+
 ![Publish container to github container registry](https://github.com/pangenome/pggb/workflows/Publish%20container%20to%20github%20container%20registry/badge.svg)
 [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](https://anaconda.org/bioconda/pggb)
 
@@ -22,7 +24,7 @@ If you have many genomes, we recommend using the [PanSN prefix naming pattern](h
 To build a graph from `input.fa`, which contains 9 haplotypes, in the directory `output`, scaffolding the graph using 10kb matches at >= 90% identity, and using 16 parallel threads for processing, execute:
 
 ```
-pggb \ 
+pggb \
     -i input.fa \
     -o output \
     -t 16 \
@@ -249,22 +251,22 @@ Although its design represents efforts to scale these approaches to collections
 It's straightforward to generate a pangenome graph by the all-pairs alignment of a set of input sequences.
 This can scale poorly, but it has ideal sensitivity.
 The mashmap/wfa alignment algorithm in `wfmash` is a very fast way to generate alignments between the sequences.
-Crucially, it is robust to repetitive sequences (the initial mash mapping step is linear in the space of the genome 
+Crucially, it is robust to repetitive sequences (the initial mash mapping step is linear in the space of the genome
 irrespective of its sequence context), and it can be adjusted using probabilistic thresholds for segment alignment identity.
 This allows us to define the base graph structure using a few free parameters: we consider the best-n candidate alignments
 for each N-bp segment, where the alignments must have at least a given identity threshold.
 
 The wfa-based alignments can break down in the case of large indels, yielding ambiguous and difficult-to-interpret alignments.
 But, we should not use such regions of the alignments directly in the graph construction, as this can increase graph complexity.
 We ignore such regions by preventing `seqwish` from closing the graph through matches less than `-k, --min-match-len` bp.
-In effect, this filter to the input to `seqwish` forces structural variations and regions of very low identity to be 
+In effect, this filter to the input to `seqwish` forces structural variations and regions of very low identity to be
 represented as bubbles. This reduces the local topological complexity of the graph at the cost of increasing its redundancy.
 
 The manifold nature of typical variation graphs means that they are very likely to look linear locally.
-By running a stochastic 1D layout algorithm that attempts to match graph distances (as given by paths) between nodes and 
-their distances in the layout, we execute a kind of multi-dimensional scaling (MDS). In the aggregate, we see that 
+By running a stochastic 1D layout algorithm that attempts to match graph distances (as given by paths) between nodes and
+their distances in the layout, we execute a kind of multi-dimensional scaling (MDS). In the aggregate, we see that
 regions that are linear (the chains of nodes and bubbles) in the graph tend to co-localize in the 1D sort.
-Applying an MSA algorithm (in this case, `abPOA` or `spoa`) to each of these chunks enforces a local linearity and 
+Applying an MSA algorithm (in this case, `abPOA` or `spoa`) to each of these chunks enforces a local linearity and
 homogenizes the alignment representation. This smoothing step thus yields a graph that is locally as we expect: partially
 ordered, and linear as the base DNA molecules are, but globally can represent large structural variation. The homogenization
 also rectifies issues with the initial wfa-based alignment.